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 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.
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.
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.