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.
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.
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.
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.