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.
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.
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.
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.