Cloud & Infrastructure Topics
Cloud platform services, infrastructure architecture, Infrastructure as Code, environment provisioning, and infrastructure operations. Covers cloud service selection, infrastructure provisioning patterns, container orchestration (Kubernetes), multi-cloud and hybrid architectures, infrastructure cost optimization, and cloud platform operations. For CI/CD pipeline and deployment automation, see DevOps & Release Engineering. For cloud security implementation, see Security Engineering & Operations. For data infrastructure design, see Data Engineering & Analytics Infrastructure.
Cloud Cost Optimization and Financial Operations
Covers strategies and organizational practices for minimizing and managing cloud and infrastructure spend while balancing performance, reliability, and business priorities. Candidates should understand cloud cost drivers such as compute, storage, data transfer, and managed services; pricing models including on demand pricing, reserved capacity commitments, savings plans, and interruptible or spot offerings; and engineering techniques that reduce spend such as rightsizing, autoscaling, storage tiering, caching, and workload placement. This topic also includes financial operations practices for continuous cost management and governance: resource tagging and cost allocation, budgeting and forecasting, chargeback and showback models, anomaly detection and alerting, cost reporting and dashboards, and processes to gate changes that affect spend. Interviewees should be able to estimate recurring costs and total cost of ownership, identify and quantify optimization opportunities, weigh trade offs between cost and business objectives, and describe tools and metrics used to monitor and communicate cost to stakeholders.
Cost Aware Architecture and Design
Focuses on how architectural decisions and design patterns affect operating cost and total cost of ownership. Interviewees should be able to reason about trade offs such as managed services versus self managed components, always on virtual machines versus event driven or serverless approaches, reserved versus on demand capacity, use of spot or preemptible instances, and multi region or edge placement. Candidates should demonstrate techniques for reducing cost through storage class selection and lifecycle policies, caching and batching, query and workload optimization, data transfer minimization, and workload isolation. The topic also covers modeling and communicating cost trade offs, estimating ongoing operating expense for alternative designs, and choosing architecture that balances budget constraints with reliability, performance, and engineering effort.
Capacity Planning and Resource Optimization
Covers forecasting, provisioning, and operating compute, memory, storage, and network resources efficiently to meet demand and service level objectives. Key skills include monitoring resource utilization metrics such as central processing unit usage, memory consumption, storage input and output and network throughput; analyzing historical trends and workload patterns to predict future demand; and planning capacity additions, safety margins, and buffer sizing. Candidates should understand vertical versus horizontal scaling, autoscaling policy design and cooldowns, right sizing instances or containers, workload placement and isolation, load balancing algorithms, and use of spot or preemptible capacity for interruptible workloads. Practical topics include storage planning and archival strategies, database memory tuning and buffer sizing, batching and off peak processing, model compression and inference optimization for machine learning workloads, alerts and dashboards, stress and validation testing of planned changes, and methods to measure that capacity decisions meet both performance and cost objectives.
Cloud Platform Fundamentals
Comprehensive understanding of core public cloud services and the primary trade offs when selecting among them across major providers such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Candidates should know compute options including virtual machines, managed compute, containers and serverless functions; storage types including object, block and file storage and lifecycle and archival strategies; managed database offerings for relational, non relational, and data warehouse workloads; networking fundamentals including virtual private networks, subnets, routing, load balancing, content delivery networks, and private connectivity; messaging and integration services such as message queues and event streaming; identity and access management and secrets management; monitoring, logging, and observability; autoscaling, elasticity, high availability, and basic disaster recovery patterns; and cost and pricing considerations. The topic also covers the trade offs between managed services and self managed infrastructure in terms of consistency, latency, cost, operational overhead, and durability, and the ability to map common workload requirements to the right service categories.
Cloud Platforms and Tooling
Practical tooling, automation, and platform specific considerations for operating and deploying workloads in public cloud environments. Topics include infrastructure as code tools and templates including vendor native templates and third party tools, account and organization structure and role based access design, deployment automation and pipeline patterns for continuous integration and continuous delivery, platform specific software development kits and command line tooling, container orchestration and serverless deployment tooling, monitoring and logging stacks and alerting, cost and billing tooling and cost optimization methods, and guidance on single cloud versus multi cloud approaches. Candidates should be able to discuss tooling selection criteria, integration between developer and operational tooling, and how tooling choices impact deployment velocity, reliability, and operational overhead.
Google Cloud Platform Deep Dive
In depth coverage of Google Cloud Platform services across compute, networking, storage, orchestration, and platform integrations. Areas include Compute Engine instance management and machine type selection, Google Kubernetes Engine concepts for container orchestration, managed databases such as Cloud SQL and Firestore, Cloud Storage features including versioning and lifecycle, networking components including Virtual Private Cloud, VPN and load balancing, content delivery with Cloud CDN, eventing and messaging with Pub/Sub, and analytics with BigQuery. Candidates should demonstrate design decisions, operational practices, scaling strategies, security and identity considerations, and service limits and trade offs for production deployments.
Network Troubleshooting and Tools
Hands on skills for diagnosing and resolving network problems using standard command line and packet analysis tools. Topics include systematic troubleshooting workflows (start with reachability tests and escalate to captures), use of ping and traceroute to verify connectivity and routing paths, netstat and ss to inspect sockets and listening ports, arp and interface commands to check layer two mappings and interface state, router and switch show commands to view routing tables and interface status, and DNS troubleshooting using nslookup and dig. Deep packet capture and analysis with tcpdump and Wireshark is covered, including capture filters, interpreting packet headers and flows, identifying retransmissions, latency sources, and protocol errors. Emphasis is on interpreting tool output, correlating results across layers, and choosing the right tool at each step of an investigation.
Multi Cloud and Hybrid Cloud Architecture
Designing systems that span multiple cloud providers or combine cloud with on premise infrastructure, including when and why to choose multi cloud or hybrid deployments and the trade offs involved. Candidates should be able to justify multi cloud and hybrid approaches with respect to vendor independence, resilience, regulatory and data residency requirements, and cost versus the increased operational and engineering complexity. Core technical concerns include workload portability strategies such as containerization and Kubernetes, platform abstraction layers and trade offs between managed vendor services and portable open source components. Data management topics include replication topologies, consistency models, disaster recovery, backup strategies, data gravity and egress cost implications. Networking and connectivity considerations cover secure cross provider links, virtual private networks, direct connections and transit architectures, service mesh and the latency and throughput implications for stateful and latency sensitive services. Identity and access management and policy consistency across providers involves identity federation, single sign on, centralized policy enforcement, and secrets and key management. Observability and operations include centralized logging, metrics and tracing, alerting, deployment and release strategies across heterogeneous environments, automation and infrastructure as code, cost governance and tagging, security boundary definition, compliance, runbooks and incident response. Interviewers commonly assess the candidate's ability to articulate concrete trade offs, propose migration and portability approaches, and recommend tooling and governance patterns to operate multi provider systems reliably.
AWS Compute and Networking
Covers design and operational knowledge of Amazon Web Services compute and network components. Candidates should understand Amazon Elastic Compute Cloud instances including instance families, sizing considerations, and pricing models such as on demand, reserved, and spot instances. Knowledge of Amazon Machine Images and launch templates, network interfaces, security groups, route tables, and Virtual Private Cloud architecture including public and private subnets, NAT gateways, and peering is expected. Expect questions on load balancing options including Application Load Balancer and Network Load Balancer, autoscaling groups and policies for availability and cost optimization, and hybrid connectivity patterns such as VPN and Direct Connect. Candidates should also be able to reason about high level multi tier application architectures on AWS, security and networking trade offs, and common infrastructure as code and automation approaches used to provision and manage these resources.
Infrastructure as Code Tools
Practical skills for authoring, deploying, and managing Infrastructure as Code templates and configurations across cloud platforms. Candidates should be able to author, read, and modify templates or configuration files for native platform tools such as AWS CloudFormation, Azure Resource Manager templates or Bicep, and Google Cloud Deployment Manager, as well as for multi cloud tools such as Terraform. Key areas include file formats such as YAML and JSON, declaring resources, passing parameters or variables, and emitting outputs, together with expressing resource dependencies, conditions, and mappings. Candidates should be able to write templates for common infrastructure patterns including networking such as virtual private clouds, subnets, and security groups, compute resources such as virtual machines and instances, and storage resources such as buckets and storage accounts. They should know how to deploy templates to create stacks or equivalent constructs, perform stack updates and change sets or plan and apply workflows, handle rollbacks and deletions, and manage state for tools that require it including remote state and state locking. Additional important skills are modularization through nested stacks or modules, template validation and linting, integration with continuous integration and continuous delivery pipelines, drift detection and remediation, and basic troubleshooting of template errors and deployment failures. Interview tasks may include writing or modifying short templates, explaining the lifecycle of a deployment, and comparing trade offs between native templates and multi cloud tooling.
Capacity Planning and Forecasting
Covers forecasting demand and planning infrastructure and platform capacity to meet expected business needs reliably and cost effectively. Candidates should be able to analyze historical usage and growth trends, build and validate capacity models, define capacity metrics and thresholds, estimate headroom and safety margins, and translate business growth scenarios into procurement or cloud provisioning plans and timelines. Includes storage and compute lifecycle planning such as archiving and retention strategies, upgrade and rollout planning to avoid disruption, and trade offs between overprovisioning and right sizing. Also addresses design for scale and redundancy, autoscaling and elasticity patterns, load balancing and failover planning, capacity testing and stress testing, monitoring and alerting for capacity signals, and techniques to measure and improve forecast accuracy. Finally it covers operational governance and decision making including cross team resource allocation, capacity reviews, cost optimization and budgeting, runbooks and change control, and alignment of capacity plans with service level objectives and business projections.
EC2 Lambda and Managed Services
Comprehensive understanding of cloud compute options and how to choose between Amazon EC2, AWS Lambda, and managed deployment services such as Elastic Beanstalk. Candidates should be able to compare tradeoffs between control and operational overhead. Amazon EC2 provides full operating system level control, support for long running and stateful processes, custom machine images, fine grain instance sizing, placement groups for network and latency requirements, and suitability for legacy or specialized workloads. AWS Lambda provides a serverless event driven execution model with automatic scaling, per invocation billing, and minimal infrastructure management but introduces cold start latency, concurrency limits, execution duration limits, and resource constraints. Managed deployment services such as Elastic Beanstalk simplify application deployment and lifecycle management by abstracting infrastructure while still allowing configuration of underlying resources. Candidates should also know instance family characteristics for sizing such as burstable T series, general purpose M series, and compute optimized C series; pricing models including on demand, reserved capacity, savings plans, and spot instances; cost optimization techniques such as right sizing and spot use; and scaling and deployment patterns including auto scaling groups, function concurrency management, scheduled scaling, placement groups, and deployment strategies. Key design considerations include stateless versus stateful architecture, startup time impact, observability and monitoring, testing and deployment complexity, security and compliance, and how each option affects reliability, latency and cost.
Governance, Policy Enforcement, and Guardrails
Implementing policy as code, compliance checking, and safety mechanisms into infrastructure systems. Topics include automated cost controls, security policy enforcement, resource naming standards, tagging strategies, and preventing common misconfigurations. Discussion of balance between flexibility and governance.
AWS Well-Architected Framework Principles
Principles and guidelines from the AWS Well-Architected Framework for designing, building, and operating robust, secure, efficient, and cost-optimized systems on AWS. Covers the five pillars—Operational Excellence, Security, Reliability, Performance Efficiency, and Cost Optimization—and related architecture decisions, patterns, and best practices.
Cloud Migration Strategy and Planning
Comprehensive planning and execution for migrating applications, data, and infrastructure from on premise environments to cloud platforms. Candidates should be able to assess existing application architecture, infrastructure, data flows, dependencies, performance and operational practices; prioritize workloads based on technical characteristics and business value; and select appropriate migration approaches such as rehost or lift and shift, replatform, refactor or rearchitect for cloud native, repurchase or move to software as a service, retire, or retain. Evaluation should include trade offs for each approach with respect to total cost of ownership, time to migrate, implementation effort, operational complexity, and long term optimization. Candidates should also plan phased migration execution including discovery and dependency mapping, migration waves, cutover and rollback strategies, and data migration and synchronization techniques. Interviewers may probe planning for domain name system updates, testing and validation, monitoring and operationalization after migration, security and compliance controls, and hybrid or coexistence patterns during transition. Candidates should be familiar with assessment tools and migration services, methods to estimate effort and risk, strategies for automation and continuous integration and continuous delivery pipelines, and training and organizational change management needed for a successful migration.
Compute and Cloud Native Architecture
Covers high level compute architecture decisions when building cloud native systems, including how microservices, containerization, and orchestration fit together. Topics include cloud native principles, serverless versus container trade offs, how containerized workloads change application and infrastructure design, deployability considerations, observability and telemetry, operational concerns when running services in managed cloud environments, and patterns for designing systems for independent scalability and fault isolation.
Infrastructure Strategy and Platform Decisions
Focuses on making technical infrastructure and platform choices with consideration for business impact and organizational factors. Topics include build versus buy trade offs, vendor and platform evaluation, scalability and reliability considerations, migration and deprecation planning for legacy systems, total cost of ownership, developer productivity impact, organizational readiness, and stakeholder involvement. Candidates should show how to structure these decisions, evaluate technical and non technical risks, and communicate clear rationale and implementation plans.
Network Performance and Latency Optimization
Network level performance considerations including bandwidth, latency, jitter, packet loss, quality of service, congestion management, and capacity planning. Techniques covered include request batching, compression, connection pooling, content delivery networks, edge caching, and transport level tuning. Candidates should also discuss measurement and monitoring of network metrics, trade offs for global user bases, and strategies to optimize tail latency for latency sensitive services.
Cloud Strategy and Migration Planning
Fundamentals and planning practices for adopting cloud computing and migrating workloads to cloud environments. Coverage includes understanding cloud delivery models such as Infrastructure as a Service, Platform as a Service, and Software as a Service, and hybrid deployment options. Candidates should be able to evaluate migration strategies including lift and shift, refactor, replatform, and rebuild, and assess trade offs across cost, performance, security, compliance, and organizational readiness. Planning topics include workload assessment, suitability analysis, vendor evaluation for examples like Amazon Web Services, Microsoft Azure, and Google Cloud Platform, migration sequencing and runbooks, data migration and networking considerations, identity and access patterns, testing and rollback strategies, monitoring and observability, cost optimization and governance, and stakeholder and change management during migration.
Server Infrastructure and Resource Allocation
Covers designing and operating server infrastructure to support applications and workloads reliably and cost effectively. Topics include server architecture and configuration choices such as memory optimized, central processing unit optimized, storage optimized, and general purpose servers and when to use each. Includes virtualization concepts and virtual machines, hypervisors, containerization technologies such as Docker, and orchestration basics such as Kubernetes at a conceptual level. Covers infrastructure provisioning and automation practices including infrastructure as code and configuration management, and how to provision physical servers, virtual machines, or cloud instances. Emphasizes resource allocation and utilization optimization through right sizing, capacity planning, monitoring, scaling strategies, load balancing, redundancy, and high availability. Also addresses network connectivity and bandwidth planning, security and access considerations, cost trade offs, and physical constraints such as power, cooling, and space when comparing bare metal, virtualized, and cloud deployments.
Containerization and Virtualization Trade Offs
Examines trade offs between containers and virtual machines and the complexity of orchestrated environments. Topics include hypervisor and virtual machine basics, container isolation and resource models, performance and overhead comparisons, security and attack surface differences, when to prefer virtual machines versus containers, single container versus orchestrated multi container setups, operational complexity versus benefits, and criteria for selecting the appropriate platform at different scales.
Network Protocols and Encapsulation
Comprehensive knowledge of network protocol stacks and layering, including the Open Systems Interconnection model and the Transmission Control Protocol and Internet Protocol suite. Candidates should understand protocol purposes and behaviors at each layer, including connection oriented and connectionless transport, address resolution, discovery and multicast management, and control plane messages. Know common protocols such as the User Datagram Protocol, Internet Control Message Protocol, and Internet Group Management Protocol and how they differ in reliability, ordering, and use cases. Be familiar with tunneling and encapsulation technologies and their tradeoffs, including Virtual Private Network, Generic Routing Encapsulation, Multiprotocol Label Switching, overlay network technologies such as Virtual Extensible Local Area Network, Generic Network Virtualization Encapsulation, and Network Virtualization using Generic Routing Encapsulation. Understand encryption and integrity options at the network layer such as Internet Protocol Security and at the transport layer such as Transport Layer Security and Secure Sockets Layer, including tunnel versus transport modes. Be able to reason about encapsulation overhead, maximum transmission unit and fragmentation, latency and throughput implications, path characteristics, compatibility and interoperability, and typical deployment patterns for site to site tunnels, remote access, data center overlays, and network virtualization.
Cloud Basics AWS Fundamentals
Basic AWS concepts relevant to DevOps: EC2 instances, S3 storage, IAM for access control, basic networking (VPCs, security groups), and understanding how cloud services differ from on-premises infrastructure.
Load Balancing and Horizontal Scaling
Covers principles and mechanisms for distributing traffic and scaling services horizontally. Includes load balancing algorithms such as round robin, least connections, and consistent hashing; health checks, connection draining, and sticky sessions; and session management strategies for stateless and stateful services. Explains when to scale horizontally versus vertically, capacity planning, and trade offs of each approach. Also includes infrastructure level autoscaling concepts such as auto scaling groups, launch templates, target tracking and step scaling policies, and how load balancers and autoscaling interact to absorb traffic spikes. Reviews different load balancer types and selection criteria, integration with service discovery, and operational concerns for maintaining availability and performance at scale.
Static Routing and IP Configuration
Configure static routes on routers to direct traffic. Understand route syntax, routing table priorities, and how to verify traffic is taking the intended path. Configure IP addresses, subnet masks, and default gateways on devices to ensure Layer 3 connectivity.
Real World Scenario Based Decision Making
Applying infrastructure knowledge to realistic business scenarios: handling traffic spikes, migrations from on-premises to cloud, optimizing costs during resource constraints, responding to security incidents, and managing infrastructure during rapid growth. Making trade-off decisions when constraints conflict.
Amazon Web Services Core Services
Comprehensive knowledge of the foundational Amazon Web Services that are commonly used to design, deploy, and operate cloud applications. This includes compute services such as Amazon Elastic Compute Cloud for virtual machines and instance families, Amazon Web Services Lambda for serverless functions, and Amazon Elastic Beanstalk for managed application platforms; storage services such as Amazon Simple Storage Service for object storage, Amazon Elastic Block Store for block volumes, and Amazon Elastic File System for shared file storage; database services such as Amazon Relational Database Service for managed relational databases, Amazon DynamoDB for NoSQL, and Amazon ElastiCache for in memory caching; networking and content delivery including Amazon Virtual Private Cloud networking concepts, subnets, security groups, load balancers, and Amazon CloudFront; container and orchestration options such as Amazon Elastic Container Service and Amazon Elastic Kubernetes Service; and management and security services including Identity and Access Management, Amazon CloudWatch monitoring and logging, Auto Scaling, and cost and service limit considerations. Candidates should understand core service characteristics, common configuration choices and trade offs, operational considerations such as high availability and fault tolerance, basic security and compliance approaches, performance and cost optimization, and guidance for selecting one service over another for typical application patterns.
Cloud Native Services Integration
Understand how to integrate Kubernetes with cloud-native services: managed databases (RDS, Cloud SQL), object storage (S3, GCS), message queues (SQS, Pub/Sub), serverless compute (Lambda, Cloud Functions), observability services (CloudWatch, Stackdriver). Know how to configure Kubernetes to securely access these services: IAM roles, IRSA (IAM Roles for Service Accounts), managed identities. Discuss when to use managed services vs Kubernetes-hosted services. Understand cost implications and operational trade-offs.
Cost Optimization and Business Justification
Covers evaluating and reducing costs across system architecture and procurement while building a financial case for change. Candidates should be able to identify cost drivers at the infrastructure and application level, including compute sizing, storage choices, data transfer, and managed service versus self managed trade offs. Expect concrete optimization techniques such as right sizing resources, using lower cost capacity options, reserved capacity, and moving to managed services when operational cost and reliability justify the premium. Also covers measurement and governance practices including cost tagging, billing analysis, budgeting, and continuous monitoring. On the business side, candidates should quantify savings and value using total cost of ownership and return on investment calculations, cost avoidance and efficiency metrics, payback period estimation, and sensitivity analysis for key assumptions. This topic includes evaluating procurement and supplier strategies, understanding short term versus long term value, balancing cost reductions with performance, maintainability, and supplier relationship health, and preparing clear cost estimates and business justification for stakeholders.
System Capacity and Cost Estimation
Translating business requirements into technical capacity needs and cost estimates for infrastructure and operations. Topics include back of envelope and Fermi estimation for storage compute and bandwidth, understanding throughput metrics such as queries per second and latency targets, provisioning strategies including autoscaling versus reserved capacity, how architectural choices drive operational cost over time, and approaches to estimate cloud and infrastructure expenses. Candidates should be able to justify assumptions, demonstrate simple calculations for resource sizing, discuss trade offs between performance reliability and cost, and explain how to align provisioning strategy with budget constraints.
Cost Analysis and Optimization Recommendations
Analyze cost implications of architectural choices. Propose cost optimizations with business case justification. Understand trade-offs between cost and other dimensions. Recognize when cost concerns should change architectural decisions.
Analyzing Requirements and Service Selection
Given a business requirement (e.g., 'store real-time game data with sub-millisecond latency'), systematically identify appropriate cloud services and justify your choice based on performance, cost, and operational considerations. Articulate trade-offs explicitly.
Containerization and Orchestration Fundamentals
Practical understanding of container technology and orchestration platforms. Topics include container image creation and registries, container runtime basics, core orchestration concepts such as pods, services, deployments, replicas, stateful sets, persistent storage, configuration management, service discovery and networking, scaling and autoscaling, rolling updates and rollbacks, self healing, resource management, and common deployment patterns. Emphasis is on how containers and orchestrators change deployment and operations practices and on at least one major platform such as Kubernetes or a cloud managed service.
Azure Compute Options and Trade Offs
Explain compute choices on Azure and the trade offs among them. Cover virtual machines for full operating system control, App Service for managed web hosting, Azure Functions for event driven serverless workloads, container instances for single container tasks, and Azure Kubernetes Service for orchestrated container platforms. For each option describe the operational responsibilities, scalability characteristics, cost model, deployment complexity, and suitability for stateful versus stateless workloads. Be prepared to justify a choice based on latency and performance needs, team expertise, deployment frequency, and cost constraints.
Storage Services: Object, Block, and File Storage
Know the main storage categories. Object Storage (S3, Azure Blob, Cloud Storage) is best for unstructured data, files, backups, and large-scale storage; it's infinitely scalable and cost-effective but accessed by key, not mounted like filesystems. Block Storage (EBS, Managed Disks, Persistent Disks) attaches to VMs; ideal for OS and database storage; provides high performance. File Storage (EFS, Azure Files, Filestore) provides shared access across multiple instances; useful for shared data and collaborative workflows. Understand use cases: Object storage for data lakes and backups; block storage for database disks; file storage for shared application data. Know that object storage pricing is very low compared to other options.
Network Equipment Selection and Justification
Choose appropriate network equipment for different design scenarios. Understand roles of different device types (access switches, distribution switches, core routers, firewalls). Consider performance requirements (throughput, latency), available ports, PoE capabilities, management features, and cost. Be able to justify why a device is appropriate for its role.
Kubernetes Troubleshooting
Covers diagnosing and resolving failures in container orchestration with an emphasis on Kubernetes pod and deployment issues. Topics include Kubernetes architecture and components, pod lifecycle and states, common failure modes such as CrashLoopBackOff, ImagePullBackOff, out of memory conditions, resource limit and quota problems, and node or scheduling issues. Practical debugging skills include using command line inspection and control tools, reading and correlating logs and events, using exec into containers, describing resources, checking node and cluster health, and interpreting health checks, liveness and readiness probes. Also includes distinguishing platform or cluster issues from application level faults, analyzing networking and service discovery problems, using monitoring and observability data, understanding rolling updates and deployment strategies, and applying remediation and rollback techniques for reliable recovery.
Cloud Infrastructure Knowledge (AWS/GCP/Azure)
Have working knowledge of at least one major cloud platform: common services (EC2/Compute Engine, RDS/Cloud SQL, S3/Cloud Storage, Load Balancers, VPCs, networking), typical failure modes, and how to troubleshoot within that platform. Understand concepts like availability zones, regions, and cross-region failover.
Build vs. Buy vs. Cloud vs. On Premise Trade Offs
Understanding key trade-offs in technology decision-making: (1) Build vs. Buy - custom development flexibility vs. packaged software speed/cost, (2) Cloud vs. On-Premise - operational burden, control, scalability, security, cost, (3) SaaS vs. Licensed - flexibility, upgrade frequency, customization options. Understanding implications for cost, time-to-value, flexibility, control, and ongoing support.
GCP Compute and Networking
Covers Google Cloud Platform compute and network services and design patterns. Candidates should know Compute Engine virtual machine types and sizing including custom machine types, persistent disk options and storage classes, and object storage concepts. Expect understanding of Virtual Private Cloud networking, subnet design, Cloud NAT, route configuration, and network security controls. Candidates should be familiar with Google Cloud load balancing options and when to choose each, autoscaling for instance groups, hybrid connectivity patterns such as VPN and Interconnect, and the practical use of the gcloud command line and infrastructure as code tools such as Deployment Manager or Terraform to provision resources. The topic also includes architecting multi tier applications on GCP and reasoning about managed services trade offs versus self managed compute.
Enterprise Cloud Architecture and Migration Strategy
Focuses on enterprise scale cloud architecture and migration planning, including multi cloud and hybrid cloud strategies, governance, cost optimization, compliance, security, and disaster recovery. Covers cloud migration patterns such as lift and shift, refactoring, replatforming, and full rearchitecting, plus data migration strategies, cutover and rollback plans, network and identity architecture, and workload placement decisions. Candidates should demonstrate understanding of differences between major cloud providers such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure, and describe how to evaluate provider strengths, select migration approaches, and design resilient, cost effective enterprise cloud solutions.
Connectivity and User Access Troubleshooting
A combined diagnostic approach for incidents where users cannot reach resources because of either connectivity issues or account and permission problems. This includes triage steps to determine whether a failure is network related, system or service related, or related to authentication and authorization; using network and system diagnostic tools such as ping, traceroute, and domain name system lookups; checking firewall and access control lists; reviewing system and application logs; verifying user account status, group membership, and permissions; and testing access from different systems and network locations to isolate variables. Candidates should demonstrate logical fault isolation, escalation reasoning, and safe verification of account and permission state.
Platform Architecture for Organizational Scale
Designing internal platforms and infrastructure to support large engineering organizations and evolving teams. Topics include developer experience and self service platform design, deployment platforms that enable safe frequent releases for hundreds of engineers, platform automation and observability patterns that provide cross service visibility, governance and operational policies, service onboarding and lifecycle, and how to evolve platform capabilities as headcount and service count grows. Candidates should discuss trade offs between centralized platform services and team autonomy, metrics for platform health, and approaches to encourage adoption while minimizing operational friction.
Networking, VPC, and Connectivity
Deep understanding of AWS VPC architecture including subnets (public and private), route tables, Network Address Translation (NAT), internet gateways, and VPC endpoints. Knowledge of security groups and network ACLs. Understanding of VPN and AWS Direct Connect for hybrid connectivity. DNS and Route 53 routing policies.
Network Monitoring and Observability
Covers strategies and tooling for observing network health and performance. Topics include active health checks versus passive telemetry, what to measure at interface and flow level, flow based telemetry such as NetFlow and sFlow and export formats such as Internet Protocol Flow Information Export, Simple Network Management Protocol based metrics, metrics hierarchy and granularity, retention and aggregation considerations, alerting strategy to manage signal to noise and avoid alert fatigue, dashboards and status pages, runbook and incident playbooks, topology and capacity planning, and common observability platforms and integrations such as Prometheus the Elastic stack and Splunk or cloud native alternatives. Interviews evaluate ability to design what to monitor how to collect it and how to turn telemetry into reliable operational signals.
GCP Core Services and Architecture Basics
Core Google Cloud Platform services and architecture concepts, including Compute Engine, Kubernetes Engine (GKE), App Engine, Cloud Functions, Cloud Run, Cloud Storage, BigQuery, Pub/Sub, Cloud SQL/Spanner, and IAM. Covers foundational cloud architecture topics such as projects and resource organization, VPC networking (regions, zones, subnets), identity and access management, security considerations, and scalable design patterns for cloud-native applications on GCP.
Azure Well-Architected Framework & Design Principles
Overview of the Azure Well-Architected Framework and its design principles, providing prescriptive guidance to architect, deploy, and operate workloads on Microsoft Azure. Emphasizes the five pillars—Cost, Operational Excellence, Performance Efficiency, Reliability, and Security—along with governance and pattern-based design considerations for Azure architectures.
Microsoft Product and Azure Fundamentals
Practical knowledge of Microsoft enterprise offerings and core Azure services, and the ability to map those products to customer scenarios. Candidates should understand when to recommend Azure compute database and networking services versus platform services when Microsoft 365 Dynamics 365 or Power Platform are more appropriate and how to articulate trade offs for scalability security compliance and cost. Topics include Azure infrastructure and platform services virtual machines app services managed databases storage networking identity and governance considerations migration patterns and common integration approaches with on premise systems.
Microsoft Products and Ecosystem
Knowledge of Microsoft's enterprise product suite and how the pieces fit together as part of a broader technology stack. Covers Office 365 and Microsoft 365 (Teams, SharePoint, Exchange), Dynamics 365, the Power Platform (Power Apps, Power Automate, Power BI), and core Azure services, along with common integration patterns between them: identity and access management (Azure AD / Entra ID, single sign-on), data flows across products, typical enterprise deployment topologies, licensing and bundling models, and security and compliance considerations. Candidates should be able to explain how these products interoperate, when a Microsoft-native service is the right fit versus a third-party alternative, and the practical tradeoffs involved in enterprise adoption, rollout, and migration.
Cloud Migration Strategies
Know common cloud migration approaches commonly summarized as the six R strategies: Rehost, Replatform, Refactor or Rearchitect, Repurchase or move to a software as a service offering, Retire, and Retain. For each approach explain the rationale, expected cost and effort, risks, and example scenarios. Be ready to describe basic migration planning steps including discovery and assessment, cost and risk evaluation, data migration techniques, cutover and rollback planning, validation testing, and post migration optimization. For entry level discussions emphasize practical examples of rehosting and replatforming and the trade offs between operational simplicity and long term modernization.
Infrastructure Coding and Optimization
Design, author, and optimize production grade code that implements infrastructure automation and runtime infrastructure behavior. Candidates should demonstrate writing maintainable and modular infrastructure as code modules or automation scripts, handling resource provisioning and scaling logic efficiently, ensuring idempotency and safe retry strategies, implementing robust error handling and timeout management, and optimizing for latency, throughput, and cost. Interviewers will evaluate clean code patterns, performance profiling and benchmarking approaches, trade offs between complexity and performance, testing strategies for infrastructure code, and how telemetry and observability feed into automated remediation and rightsizing decisions.
Cloud Governance and Architecture Standards
Design and evaluate governance frameworks and reference architectures that balance speed and safety. Topics include architecture review boards, approval workflows for changes and exceptions, escalation paths, guardrails and enforcement, policy as code, and metrics for monitoring compliance. Define reusable reference patterns for service decomposition data pipelines and application programming interface gateway conventions along with deployment observability and security baselines that teams can adopt or request exceptions for. Explain trade offs between centralization and team autonomy and practical enforcement strategies to drive consistent outcomes.
Cloud Migration Strategy and Cutover
Plan end to end migration using common patterns such as rehost replatform refactor and repurchase and map those choices to workload characteristics. Define sequencing and phase gates plan pilot migrations and develop detailed cutover playbooks that include data replication validation reconciliation and rollback triggers. Address zero downtime techniques such as parallel running incremental synchronization canary deployments and feature gates plus testing strategies metrics to detect regressions and run books for rollback and post cutover verification.
Network Design and Security Architecture
Design secure and reliable network and security architectures for cloud environments. Topics include virtual network and subnet design, segmentation and firewall policies, private connectivity patterns, load balancing and failover, encryption in transit and at rest, key management fundamentals and integration with key vaults, identity and access management boundaries, denial of service protections, network monitoring and logging, and compliance and regulatory controls such as data residency and auditability. Interviewers look for practical trade offs between usability security and operational complexity.
Azure Core Services Overview
Demonstrate familiarity with the primary categories of Azure services and the common offerings within each category. Explain compute options such as virtual machines, App Service, and Azure Functions; storage options such as blob storage, file shares, queue and table storage; networking constructs such as virtual networks and load balancers; and database offerings such as Azure SQL Database, Azure Database for PostgreSQL, and Cosmos DB. Also be familiar with security and identity tools like Azure Key Vault and role based access control, and with monitoring solutions such as Azure Monitor and Application Insights. For each category explain typical use cases, operational trade offs, and basic selection criteria based on control, scalability, cost, and maintenance burden.
Kubernetes and Container Orchestration Fundamentals
Core knowledge of container orchestration with a focus on Kubernetes fundamentals and everyday operations. Candidates should understand the role of orchestration and why it is used at scale, and be familiar with core Kubernetes objects including pods as the smallest deployable unit, deployments and replica management, replica sets, services for inter pod communication and service discovery, config maps and secrets for configuration and secret management, and the basic lifecycle of containerized applications. Coverage includes deployment and scaling workflows, rolling update and rollout strategies including rollbacks, common deployment patterns such as blue green and canary, and basic health checks and probe concepts. Practical operational skills include common kubectl commands for deploying, inspecting, scaling and debugging applications, reading pod logs and events, describing resources, port forwarding, and diagnosing common failure modes. Candidates should also understand high level trade offs between using managed cloud provider Kubernetes services and operating self managed clusters including operational overhead, upgrades, cost, and reliability considerations. The scope focuses on fundamentals and day to day operations and excludes advanced cluster administration topics such as custom operators, admission controllers, control plane internals, and deep platform level configuration.
Large-Scale Consumer Device Infrastructure and Operations
Assess a candidate's understanding of the scale, constraints, and distinctive operational challenges of running infrastructure for a company that manufactures and ships large volumes of consumer hardware devices, from managing hundreds of millions to billions of device identities to building a global data center and edge footprint that keeps those devices online and updated. Areas to address include privacy first architecture and telemetry minimization, staged device update and distribution rollouts, secure device attestation and key management, zero trust segmentation and defense in depth, and capacity planning for product launch spikes and other sudden demand surges. Interviewers expect references to concrete infrastructure trade offs and design patterns such as regional edge caching, multi region replication and consistency choices, canary and staged rollout strategies for firmware or software updates, and operational practices for safe change rollout and incident response when a defect or outage can affect millions of devices simultaneously. The assessment evaluates systems thinking across hardware and software boundaries, cross functional collaboration, and the ability to propose scalable, privacy aware, resilient designs that hold up at consumer device scale.
Strategic Roadmap Planning
Planning multi year strategic roadmaps that balance innovation and operational stability, prioritize work against limited resources, and align technical or product decisions with business outcomes. Candidates should be able to describe prioritization frameworks and criteria, how to estimate and allocate resources across teams, capacity and cost forecasting, phased rollout and migration strategies, trade off analysis between addressing technical debt and new investment, and approaches for securing executive buy in and cross functional alignment. Interviewers assess strategic thinking, measurable milestone definition, risk mitigation, and the ability to translate strategy into program level execution.
Wide Area Network Design and Optimization
Focuses on designing and tuning intersite connectivity for multi site architectures. Candidates should cover connectivity options and tradeoffs including internet based virtual private networks, dedicated circuits and multiprotocol label switching, software defined wide area networking overlays, and direct cloud connectivity. Expect discussion of route distribution and interaction with interior and exterior routing, path selection, traffic engineering, quality of service end to end across the wide area, latency and jitter mitigation, bandwidth and capacity planning, failover and site reachability strategies, and techniques for accelerating or caching traffic. Candidates should explain operational concerns such as monitoring, health checks, failover testing, and cost versus performance tradeoffs when choosing designs.
Network Troubleshooting and Root Cause Analysis
Covers structured, multi layer approaches to diagnosing complex network and cross component failures. Candidates should demonstrate how they characterize incidents, assess scope and impact, reproduce problems when safe, form testable hypotheses, and apply systematic elimination to isolate the root cause. Expect discussion of packet capture analysis, path and flow troubleshooting, interpreting router and switch show commands, checking forwarding information and ARP tables, correlating syslog and application logs, verifying Domain Name System and name resolution, and identifying configuration errors, access control or firewall blocks, routing and forwarding anomalies, MTU and performance issues, and intermittent faults. Candidates should describe tooling and telemetry patterns they use to validate hypotheses, how they collaborate with application and infrastructure teams to eliminate components, how they document findings, and how they drive remediation and post incident reviews to prevent recurrence.
Managed Services and Self Managed Tradeoffs
Compare managed cloud services with self managed alternatives and explain when to choose each based on scale cost operational burden and required control. Cover operational ownership upgrade and patch cadences security responsibilities portability and vendor lock in risk performance characteristics and debugging and observability differences. Use concrete workload examples such as managed databases managed event services serverless compute and self hosted containers or virtual machines to justify decisions and to surface long term operational and migration implications.
Cloud Migration and Implementation Roadmap
Designing realistic, phased migration strategies and implementation plans that move customer workloads to the cloud while minimizing downtime, cost, and operational risk. Candidates should cover migration patterns such as lift and shift, replatforming, and refactoring, data migration and synchronization approaches, cutover and rollback plans, validation and testing strategies, pilot and proof of concept design, integration with existing systems, automation and runbook creation, operational readiness, and measurable success criteria. Answers should show how to sequence work, mitigate risks, and reduce business disruption across people process and technology dimensions.
Cloud Architecture Trade-Offs and Design Thinking
Reason about architectural trade offs and design choices for cloud native systems. Candidates should articulate frameworks for comparing managed services with self hosted solutions, weigh consistency versus availability requirements, evaluate cost versus performance trade offs, select appropriate region and replication strategies, and consider operational complexity and failure modes. Interviewers expect clear articulation of constraints, metrics used to make decisions, estimation of costs and latencies, and demonstration of iterative design thinking that balances product needs, delivery speed, and long term maintenance.
Cloud Service Selection and Tradeoffs
Frameworks and criteria for evaluating cloud platform services and making pragmatic architecture decisions. Coverage includes managed versus self managed offerings, choice of compute such as serverless functions versus containers versus virtual machines, storage class and database type selection, networking and regional availability, security and compliance considerations, operational burden and run book requirements, vendor lock in and portability risks, performance and latency trade offs, and cost modeling and total cost of ownership. Candidates should be able to articulate trade offs, provide examples of making service choices for real world constraints, and explain how selection affects operability and future evolution.
Infrastructure Strategy and Roadmap
Create a medium term infrastructure vision and two to three year roadmap aligned with business trajectory. Include capacity planning cost projections multi region and data locality strategy shared platform investments developer tooling observability and reliability priorities security and compliance milestones staffing and operating model changes. Provide prioritized milestones success metrics phase gates and risk mitigation strategies and explain how the roadmap adapts to new product initiatives and geographic expansion.
Cloud Integration and Hybrid Network Architecture
Designing connectivity and integration between on premise infrastructure and public cloud environments, including hybrid and multi cloud topologies. Topics include dedicated interconnect options and internet based connectivity, routing and network topology trade offs, virtual private networks and software defined wide area networks, network virtualization, security boundary and identity considerations across environments, latency and throughput implications, cost trade offs for interconnects, and operational practices for managing hybrid infrastructure and multi cloud complexity.
Infrastructure as Code Design and Modularity
Deep expertise with Terraform, CloudFormation, or Pulumi at scale. Module design patterns, code organization across monorepos vs polyrepos, state management strategies, remote backends, workspaces, and dependency management. Version control strategies for infrastructure, handling breaking changes, and managing IaC across teams. DRY principles and reducing repetition across infrastructure definitions.
Infrastructure Scaling and Capacity Planning
Operational and infrastructure level planning to ensure systems meet current demand and projected growth. Topics include forecasting demand headroom planning and three to five year capacity roadmaps; autoscaling policies and metrics driven scaling using central processing unit memory and custom application metrics; load testing benchmarking and performance validation methodologies; cost modeling and right sizing in cloud environments and trade offs between managed services and self hosted solutions; designing non disruptive upgrade and migration strategies; multi region and availability zone deployment strategies and implications for data placement and latency; instrumentation and observability for capacity metrics; and mapping business growth projections into infrastructure acquisition and scaling decisions. Candidates should demonstrate how to translate requirements into capacity plans and how to validate assumptions with experiments and measurements.
Cost Optimization at Scale
Addresses cost conscious design and operational practices for systems operating at large scale and high volume. Candidates should discuss measuring and improving unit economics such as cost per request or cost per customer, multi tier storage strategies and lifecycle management, caching, batching and request consolidation to reduce resource use, data and model compression, optimizing network and input output patterns, and minimizing egress and transfer charges. Senior discussions include product level trade offs, prioritization of cost reductions versus feature velocity, instrumentation and observability for ongoing cost measurement, automation and runbook approaches to enforce cost controls, and organizational practices to continuously identify, quantify, and implement savings without compromising critical service level objectives. The topic emphasizes measurement, benchmarking, risk assessment, and communicating expected savings and operational impacts to stakeholders.
Infrastructure Automation and Provisioning
Covers designing, implementing, and operating automated infrastructure provisioning and configuration using Infrastructure as Code practices and complementary automation patterns. Candidates should be able to select and author declarative infrastructure definitions with tools such as Terraform, CloudFormation, and Azure Resource Manager templates, and discuss configuration management tools such as Ansible, Puppet, or Chef. Core skills include modular and reusable code organization for multiple environments, variable and output management, remote state management and locking, idempotency and atomicity of operations, and version control integration for infrastructure artifacts. Candidates should understand testing and validation practices including linting, plan or dry run validation, unit and integration testing of infrastructure changes, and drift detection and remediation. The topic includes strategies for safe changes and rollbacks, change coordination, error handling and recovery, and deployment patterns such as canary and blue green where applicable. It also encompasses automation and orchestration patterns, immutable infrastructure and self healing practices, autoscaling and scaling policies, automated patching and updates, secrets handling patterns using secret managers, and integrating observability and monitoring into automated workflows. Finally, candidates should be able to reason about trade offs between imperative and declarative approaches, scaling Infrastructure as Code across large projects and teams, and security and compliance considerations for automated provisioning.
Technology and Platform Selection
Evaluation and justification of technologies services and platforms used to implement systems across the stack. Candidates should be able to select compute options including virtual machines containers and serverless platforms as well as orchestration and workflow engines messaging systems batch and streaming processing engines object and block storage data warehouses and other data platforms. The topic encompasses comparing managed services and self managed deployments cloud versus on premise hosting and choosing frameworks runtimes and overall stacks based on workload characteristics. Assessment focuses on weighing trade offs across cost operational overhead reliability latency and throughput scaling characteristics vendor lock in development velocity team familiarity and learning curve maturity and community support security and compliance and monitoring and debugging complexity. Candidates should demonstrate how system requirements map to service capabilities justify build versus buy decisions and managed service choices design proof of concept experiments and outline migration and rollout planning while making pragmatic choices that balance performance cost and operational risk.
Azure Services Deep Dive
In depth knowledge of Microsoft Azure platform services, architecture trade offs, operational considerations, and best practices. Candidates should be able to discuss compute options such as Azure Virtual Machines sizing, availability sets and zones, maintenance windows, and scale sets; storage options including Azure Storage account types, redundancy and replication choices, and backup strategies; database offerings such as Azure SQL Database, managed instances, high availability and replication patterns; networking fundamentals including Azure Virtual Network design, subnetting, network security groups, peering, service endpoints and private links, and load balancing with Application Gateway and Azure Load Balancer; container orchestration and related considerations such as Azure Kubernetes Service concepts, cluster sizing, upgrades, and networking models; serverless and platform services such as Azure Functions and event driven architectures; infrastructure as code and deployment pipelines using Azure DevOps and templates; identity and secrets management with Azure Active Directory and Azure Key Vault including secret rotation; monitoring, logging, and cost optimization strategies; and security, compliance, and disaster recovery planning across these services. Interview evaluation focuses on architectural reasoning, trade off analysis, operational runbook concerns, scaling and performance tuning, failure modes and recovery, and automation using provider native tooling.
Cloud Platform Experience
Personal account of hands on experience using public cloud providers and the concrete results delivered. Candidates should describe specific services and patterns they used for compute, storage, networking, managed databases, serverless and eventing, and explain their role in architecture decisions, deployments, automation and infrastructure as code practices, continuous integration and continuous delivery pipelines, container orchestration, scaling and performance tuning, monitoring and incident response, and cost management. Interviewees should quantify outcomes when possible with metrics such as latency reduction, cost savings, availability improvements or deployment frequency and note any formal training or certifications. This topic evaluates depth of practical experience, ownership, and the ability to operate and improve cloud systems in production.
Amazon Web Services Architecture and Operations
Advanced knowledge of Amazon Web Services platform services, architectural patterns, operational best practices, and trade offs. Candidates should be able to justify compute choices such as Amazon Elastic Compute Cloud instance types, instance sizing and performance tuning, and Auto Scaling strategies; storage and durability decisions including Amazon Simple Storage Service storage classes, versioning, lifecycle management, replication and archival strategies; database patterns such as Amazon Relational Database Service with multi availability zone deployments, read replicas and failover behavior, and Amazon DynamoDB capacity modes and throughput trade offs; networking design including Amazon Virtual Private Cloud topology, subnet and routing strategies, peering, gateway and interface endpoints, and network security controls; infrastructure as code and deployment patterns using Amazon CloudFormation including stack management and automated rollbacks; serverless and event driven design such as Amazon Web Services Lambda concurrency and cold start considerations and integration with Amazon API Gateway; content delivery and caching with Amazon CloudFront and Amazon ElastiCache including cache invalidation and expiry strategies; service specific operational concerns such as rate limiting, backup and restore, monitoring, logging, alerting and incident response; and cross cutting concerns including identity and access governance, cost optimization, disaster recovery planning and testing, and automation. Interview focus is on design reasoning, anticipating failure modes, scaling strategies, performance tuning, observability and automation, and provider specific operational practices.
Infrastructure as Code and Configuration Management
Infrastructure as Code and Configuration Management covers designing, implementing, and operating infrastructure defined as code and the practices that keep that infrastructure consistent and auditable. Topics include Infrastructure as Code principles and patterns, declarative versus imperative approaches, idempotency, state management, module and template design for reusable infrastructure, version control integration, testing and validation of infrastructure code, drift detection and remediation, safe rollout and rollback strategies, policy as code and Git operations for infrastructure, and how to scan and remediate infrastructure misconfigurations. This topic also encompasses integration points with system configuration tools and considerations for managing secrets and secure defaults within infrastructure definitions.
Cloud Architecture Fundamentals
Fundamental concepts and design patterns for cloud based systems and services. Topics include core service categories such as compute, storage, networking and databases, virtual machines and containers, serverless computing, managed services, and infrastructure as code. Understand deployment and service models including infrastructure as a service, platform as a service, and software as a service. Evaluate architectural patterns including monolithic, microservices, and serverless approaches, and how they influence scalability, availability, reliability, performance, security, and cost. For more senior roles include distributed systems concepts, consistency and partitioning models, trade off analysis, fault isolation, observability and operational practices in cloud native design.
Understanding the Company's Infrastructure Context
Research the company's public infrastructure information (engineering blog, tech talks, published case studies, job description). Understand what systems they operate at scale, what problems they likely face, and what your role would contribute to.
Technical Vision and Infrastructure Roadmap
This topic assesses a candidate's ability to define a multi year technical vision for infrastructure, platform, and systems and to translate that vision into a practical execution roadmap. Core skills include evaluating technology choices and architecture evolution, planning migration and modernization paths, anticipating scalability and capacity needs, and balancing cost performance with resilience and operational reliability. Candidates should demonstrate approaches to managing technical debt, sequencing investments across quarters and releases, estimating resources and timelines, establishing measurable infrastructure goals and key performance indicators, and implementing governance and standards. Discussion may also cover reliability and observability, security and compliance considerations, trade offs between short term stability and long term rearchitecture, prioritization to enable business outcomes, and communicating technical trade offs to both technical and non technical stakeholders.
Networking Fundamentals and Troubleshooting
Comprehensive coverage of core computer networking principles and the practical diagnostic and operational skills required to design, operate, and troubleshoot production systems. Fundamental concepts include the Open Systems Interconnection model layers, the Transmission Control Protocol and the Internet Protocol stack, the User Datagram Protocol, socket and port semantics, address notation and subnetting, Network Address Translation, Dynamic Host Configuration Protocol, and the Domain Name System resolution process. Infrastructure and architectural topics include switching and virtual local area networks, routing concepts and routing table behavior including Border Gateway Protocol basics, load balancing strategies and failure modes, firewall and access control, virtual private network technologies, and container and service network communication patterns. Diagnostic and tooling skills cover connectivity testing and path analysis, process and socket inspection, packet capture and analysis, and common command line tools and utilities used for network investigation. Performance and reliability topics include latency, bandwidth and throughput, packet loss, congestion and congestion control, connection pooling, timeout and retry strategies, and approaches to optimization. Observability, monitoring, and security practices include collecting and interpreting network metrics, logs, and traces, using packet capture tools for root cause analysis, and understanding how network issues surface in distributed applications. At senior levels expect discussion of network performance tuning, capacity planning, load balancer behavior at scale, and design decisions that affect system reliability and security.
Cost Optimization for Mobile Services
Strategies to reduce both operational and user facing costs while maintaining acceptable user experience. Topics include minimizing network payloads and request frequency, efficient caching and cache invalidation, content delivery network strategies, client side compression and bundling, trade offs between offline support and server calls, right sizing backend compute and storage, pricing and quota considerations, telemetry for cost monitoring, and approaches to detect and mitigate cost anomalies.
Infrastructure Implementation and Operations
Hands on design, deployment, and operational management of infrastructure components and services. This includes setting up and configuring load balancers, database replication and high availability, caching layers, networking and network security, service discovery and routing, container deployment and orchestration, monitoring and observability, logging and alerting, backup and disaster recovery strategies, and secrets management in runtime. Candidates should be able to walk through concrete implementations, explain trade offs, demonstrate troubleshooting and performance tuning, and show how infrastructure components integrate to meet availability, scalability, and security requirements.
Systems and Infrastructure Experience
Describe and analyze your hands on experience designing, operating, and maintaining infrastructure and systems. Candidates should be prepared with three to four concrete examples of systems or infrastructure projects they directly contributed to, including quantitative scale metrics such as user counts, requests per second, data volumes, throughput, and geographic distribution. Discuss architecture decisions and trade offs, component choices, platform boundaries, and how the design met requirements for scalability, reliability, performance, and security. Cover operational aspects such as deployments, configuration management, automation and infrastructure as code, monitoring and observability, incident response and remediation, capacity planning, and disaster recovery and business continuity. Include experience with large scale and multi region deployments, data center operations, networking at scale, and integration points. Also cover enterprise information technology topics where relevant, for example servers and endpoints, storage systems, networking hardware, identity and access infrastructure such as Active Directory, firewalls, routers and switches, and the differences and migration considerations between on premise and cloud infrastructure. Be ready to explain specific challenges faced, how issues were diagnosed and resolved, trade offs made, and the candidate's exact role and contributions.
Cloud Monitoring and Troubleshooting
Monitoring, logging, and troubleshooting in cloud environments. Covers cloud provider monitoring services and patterns such as CloudWatch, Azure Monitor, and Google Cloud Monitoring; setting up metrics and alarms; centralized logging and log analysis in cloud contexts; health checks and auto recovery; diagnosing common cloud issues like performance degradation, networking and permissions problems, and resource exhaustion; and best practices for instrumenting cloud native services.
Domain Name System Resolution and Troubleshooting
Deep understanding of the Domain Name System resolution process and the related operational concerns. Topics include the roles of recursive resolvers and authoritative name servers, zone delegation and glue records, common record types such as address records, canonical name records, mail exchange records, name server records, text records, and service records, and how time to live values affect caching and propagation. Candidates should be able to explain frequent failure modes such as misconfigured records, propagation delays, delegation mistakes, name server outages, and secure configuration problems, and demonstrate troubleshooting techniques using query tools and packet capture to trace queries and determine whether issues stem from authoritative sources, caches, transport, or configuration.
Routing Fundamentals and Diagnostics
Core routing concepts and practical diagnostics for both control plane and data plane issues. Coverage includes static versus dynamic routing, prefix notation and classless interdomain routing, routing tables, default routes, longest prefix selection, route summarization and redistribution, and policy based routing. Candidates should understand convergence and failover behavior and be familiar with routing protocols such as Border Gateway Protocol for interdomain routing and Open Shortest Path First for intradomain routing, including path selection attributes and techniques such as local preference, autonomous system path preference, multi exit discriminator, and equal cost multipath. Diagnostic topics include traceroute analysis, route advertisement inspection, route table validation, and troubleshooting common faults like route leaks, blackholes, incorrect next hops, and aggregation errors.
Cloud Cost Modeling and Planning
Building and evaluating cost models for cloud adoption and migration, including estimating total cost of ownership, comparing on premise and cloud deployment costs, and developing business cases for cloud investments. Skills include mapping workload characteristics to cloud pricing models, forecasting usage and growth, accounting for licensing and migration one time costs, modeling ongoing operational costs, and incorporating discounts such as reserved capacity and spot pricing. Also covers scenario and sensitivity analysis, capacity planning and rightsizing, cost governance practices such as tagging and showback or chargeback, measuring financial outcomes and return on investment, and strategies for post migration cost optimization and financial monitoring.
Cloud Platforms and Infrastructure
Comprehensive understanding of cloud computing platforms and core infrastructure concepts. Candidates should know service models including Infrastructure as a Service, Platform as a Service, and Software as a Service, and be familiar with major providers such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Core technical knowledge includes compute models, storage systems, networking fundamentals such as domain name system and load balancing, virtual private networks and network segmentation, virtualization, containerization for example Docker, orchestration with Kubernetes, serverless architectures, and microservices. Candidates should be able to evaluate trade offs between managed services and self managed solutions with respect to cost, reliability, operational burden, scalability, performance, security, and vendor lock in, and reason about when to choose platform managed services versus building custom infrastructure. The topic also covers system design considerations for high availability and fault tolerance, capacity planning and autoscaling, monitoring and observability, deployment strategies, and operational practices such as infrastructure as code and continuous integration and continuous delivery. This knowledge is critical for backend engineers, site reliability engineers, and DevOps roles and is increasingly relevant across many engineering positions.
Cloud and Infrastructure Architecture
Design of cloud and infrastructure solutions from simple cloud applications to end to end cloud systems and large scale infrastructure. Topics include stateless versus stateful design, VPC and networking basics, security groups and firewalls, multi environment management, deployment topology, capacity planning, global distribution, cloud native services, infrastructure as code, and operational concerns for sustaining large scale cloud environments.
Vendor Selection and Technology Evaluation
Approach for evaluating vendors, tools, and technology options before committing to them. Topics include defining requirements and success criteria, creating structured evaluation and scoring criteria, running proof of concept or pilot exercises, benchmarking performance and scalability, assessing total cost of ownership and ongoing operational burden, reviewing service level agreements and support/contract terms, examining vendor roadmaps and interoperability with existing systems, and planning for migration paths and vendor lock in mitigation. Applies to any technology or service selection decision (infrastructure and network hardware, cloud platforms, data and software tooling, security products, SaaS vendors, and similar), not just one domain.
Cloud Migration and Modernization
Covers approaches and planning for moving workloads and data to the cloud and modernizing legacy systems. Candidates should be able to explain migration patterns such as lift and shift, replatforming, and refactoring; how to conduct discovery and assessment; strategies for database and data transfer including bulk migration and change data capture; application modernization options including containerization and managed platform services; cutover planning, rollback and validation techniques; tooling and automation to reduce risk; and how to evaluate cost, risk, governance, security, and return on investment for a phased modernization program.
Infrastructure Fundamentals
Foundational infrastructure and system components that underpin modern application architectures. Topics include relational and non relational database trade offs, application programming interfaces such as representational state transfer and remote procedure call frameworks, caching layers including Redis and Memcached, load balancers and their layer four and layer seven behaviors, message queues and asynchronous processing patterns, and containerization and orchestration technologies such as Docker and Kubernetes. Candidates should understand each component's purpose, how components interact in an end to end data flow, common failure modes and mitigation strategies, and operational concerns including deployment and rollback strategies, health checks, monitoring, logging, metrics and alerting. Important technical trade offs to reason about include latency and throughput implications, scalability patterns, consistency and durability properties, delivery semantics and idempotency, backpressure and retry strategies, dead letter queues, caching patterns and invalidation, and capacity planning and cost considerations. Interview questions typically probe component selection for given requirements, design choices to improve reliability and maintainability, and how these components fit together in real architectures.
Azure and Google Cloud Platforms
Knowledge of core cloud platform concepts and the managed services offered by Microsoft Azure and Google Cloud Platform. Candidates should understand compute options including virtual machines, managed container services, and serverless functions; platform as a service offerings; managed relational and non relational databases; object, block, and file storage; and messaging and eventing services. They should know networking fundamentals such as virtual private cloud or virtual networks, subnets, load balancing, peering, routing, and firewall rules, as well as identity and security topics including identity and access management, role based access control, key management, and encryption. Candidates must be able to map and compare common service equivalents across providers, for example Azure Virtual Machines to Google Compute Engine, Azure App Service to Google App Engine, Azure SQL to Cloud SQL, Azure Blob Storage to Cloud Storage, Azure Service Bus to Cloud Pub Sub, Azure Cosmos DB to Cloud Firestore or Bigtable, and Azure Kubernetes Service to Google Kubernetes Engine. The description also covers operational considerations such as region and zone architecture, high availability and fault tolerance, autoscaling, monitoring and logging, cost and billing differences, service limits and quotas, infrastructure as code and deployment tooling, and multi cloud trade offs including latency, data egress costs, compliance, and vendor lock in. Interview questions typically ask candidates to translate architectures and patterns between platforms, justify service choices, and explain migration or interoperability strategies.
Software Defined Networking Concepts
Explain the principles of software defined networking and the operational and architectural implications of separating control functions from forwarding functions. Topics include centralized and distributed controller models, network virtualization and overlay networks such as virtual extensible local area network, programmability and northbound and southbound interfaces, network function virtualization, orchestration and automation, and the security and reliability trade offs introduced by moving intelligence into software layers. Interviewers assess when software defined networking improves agility and scale and where traditional hardware approaches are preferable.
Kubernetes Architecture and Core Components
Fundamental architecture of Kubernetes and its core components: control plane design including API server, etcd distributed state store, scheduler, controller manager, and cloud controller manager; worker node components including kubelet, kube proxy, and the container runtime; core objects and abstractions such as pods, services, deployments, stateful sets, daemon sets, jobs, config maps, and secrets; cluster state management, service discovery, health checks, and self healing. Candidates should understand how components interact, data flow, failure modes, and how to design for availability and scalability at the component level.
Cloud Architecture and Design Patterns
Designing and evaluating cloud native architectures and common architecture patterns, with attention to service capabilities, limits, and operational and security implications. Topics include compute scaling models for virtual instances, managed compute, serverless functions and container orchestration; storage choice, object storage classes, lifecycle policies and caching strategies; managed relational database architectures with high availability patterns such as multiple availability zone deployments and read replicas; trade offs of serverless approaches including cold start and invocation limits; different load balancing approaches for application level and network level traffic; networking and identity boundary design including virtual networks, subnetting, routing, security groups and access control patterns; backup, recovery and disaster recovery planning; deployment patterns such as blue green and canary releases; scalability strategies, performance and latency considerations; vendor lock in, portability and total cost of ownership trade offs; and operational practices for monitoring, limits management and incident response. Candidates should be able to translate requirements into architecture decisions, justify trade offs, and design for resilience, scalability, performance, security and cost.
Network Architecture and Design
Comprehensive principles and practical patterns for designing reliable, scalable, and secure networks across enterprise, data center, and wide area environments. Covers topology choices such as spine and leaf, hub and spoke, star, mesh, ring, and hybrid topologies, and how those choices affect resilience, latency, and scalability. Includes hierarchical design patterns for access distribution and core layers, device roles such as switches, routers, and load balancers, and cloud and data center specific patterns. Details redundancy and high availability strategies including active active and active passive failover, redundancy sizing such as N plus one, failure modes, convergence behavior, and operational implications for service level agreements. Addresses capacity planning, performance trade offs, quality of service and traffic engineering, routing and switching fundamentals and routing protocol behavior, segmentation for performance and security including virtual local area networks and subnets, and placement of security controls. Emphasizes trade offs between bandwidth, latency, cost, complexity, manageability, and operational burden, and how to select topology and design patterns for different application, performance, and operational requirements.
Cloud Service and Deployment Models
Comprehensive coverage of the primary cloud service and deployment choices and the decision criteria behind them. Explain Infrastructure as a Service, Platform as a Service, and Software as a Service, including responsibility boundaries, operational implications for security, patching, and management. Explain public cloud, private cloud, and hybrid cloud deployment types and the trade offs in control, isolation, scalability, cost, and compliance. Describe decision factors and example scenarios for selecting a service model or deployment type based on application characteristics such as multi tenancy, latency requirements, data sensitivity, regulatory constraints, integration with on premise systems, and team skills. Discuss vendor lock in, portability and migration implications, and how these choices affect cost, operational burden, resilience, and monitoring.
Compute Options and Trade Offs
Covers the core compute models and how to evaluate them for application workloads. Candidates should be able to explain virtual machines where operators manage the operating system and runtime and which provide maximum control but require operating system patching capacity planning and infrastructure maintenance. Candidates should understand container technologies and container orchestration patterns that enable packaging portability and efficient resource use for microservices while introducing operational concerns around orchestration networking storage and deployment. The topic includes managed platform as a service offerings that abstract runtime and deployment responsibilities to reduce infrastructure management at the cost of lower level control and customization. It also covers serverless function models that provide event driven automatic scaling and pay per execution billing while presenting constraints such as cold start latency execution time limits and challenges for long running or highly stateful workloads. Candidates should know instance type selection and resource profiles such as general purpose compute optimized and memory optimized options; autoscaling strategies and performance and cost trade offs; startup latency and cold start implications; state management and persistence patterns; monitoring and observability complexity; security and operational responsibilities; and how team expertise application architecture and cost considerations influence the best choice of compute option for a given workload.
Observability and Monitoring Architecture
Designing and architecting end to end observability and monitoring systems that scale, remain reliable under load, and do not become single points of failure. Topics include deciding which telemetry to collect and why including metrics logs traces and events, instrumentation strategies, collection models such as push versus pull, high throughput telemetry ingestion and pipeline design, time series storage and compression, aggregation and partitioning strategies, metric cardinality and retention tradeoffs, distributed tracing propagation and sampling strategies, log aggregation and secure storage, selection of storage backends and time series databases, storage tiering and cost optimization, query and dashboard performance considerations, access control and multi tenancy, integration with deployment pipelines and tooling, and design patterns for self healing telemetry pipelines. Senior level assessments include designing scalable ingestion and aggregation architectures, storage tiering and query performance optimization, cost and operational tradeoffs, and organizational impacts of observability data.