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I started with VMs and worked outward which felt logical to me but in hindsight I spent too long on stuff they probably already knew.
Structure your answer around the three pillars—compute, storage, and networking—and for each, briefly describe the main models, then highlight trade-offs like control vs. abstraction, cost, and operational overhead. Use a concrete example (e.g., a web app) to tie the building blocks together and show how they interact.
Pro tip: Emphasize that the right choice depends on workload characteristics and team maturity—demonstrating that you consider non-functional requirements like scalability, cost, and operational burden will set you apart. Also, mention that these primitives are consistent across major clouds (AWS, Azure, GCP), showing you understand the underlying concepts rather than just one vendor.
Briefly state that public clouds are built on three fundamental pillars: compute, storage, and networking, and that each offers a spectrum of abstraction levels. This frames your answer and shows you can organize complex topics.
Describe VMs (full control, manual scaling), containers (portable, efficient, need orchestration), and serverless (event-driven, auto-scaling, no server management). For each, mention a typical use case and a key trade-off (e.g., cost vs. control).
Cover object storage (unstructured, scalable, cheap), block storage (low-latency, attached to VMs), and file storage (shared, hierarchical). Highlight when to use each, such as object for static assets, block for databases, file for shared configs.
Explain VPCs (isolated networks), subnets (public/private segmentation), security groups (instance-level firewalls), and load balancers (traffic distribution). Mention how they enable secure, scalable architectures.
Walk through a simple web application: serverless functions for APIs, object storage for static content, a VPC with public/private subnets, and a load balancer. This demonstrates how the building blocks compose in practice.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the service's requirements—traffic patterns, latency sensitivity, scaling needs, and team operational maturity—then map those to the strengths of VMs, containers, and serverless. Walk through the trade-offs explicitly and conclude with a recommendation that balances technical fit with organizational constraints.
Pro tip: Acknowledge that the 'right' answer depends on context and that many production systems use a mix; showing you can reason about trade-offs rather than dogmatically pick one technology signals senior-level judgment.
Ask about expected traffic volume, latency requirements, scaling patterns, statefulness, and team expertise. These factors will drive the decision more than any generic best practice.
For VMs, consider full control, legacy compatibility, and predictable pricing. For containers, consider portability, density, and orchestration overhead. For serverless, consider auto-scaling, pay-per-use, and cold-start latency.
Discuss operational complexity, cost model, scalability, cold starts, vendor lock-in, and debugging/monitoring differences. Be explicit about which trade-offs matter most for this service.
Mention that the choice isn't permanent—you might start with containers for flexibility and later move to serverless for spiky workloads, or use VMs for stateful components alongside containers.
State your preferred option based on the clarified requirements, and explain why it's the best fit. Acknowledge any residual risks and how you'd mitigate them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went unit to integration to load to chaos to canary in order.
Structure your answer as a layered testing pyramid, starting with fast unit tests and progressing to integration, end-to-end, and finally production-focused techniques like canary deployments and chaos engineering. Emphasize how each layer balances cost, speed, and confidence, and how they work together to catch issues early while validating real-world resilience.
Pro tip: Tie each testing layer to a specific failure mode it addresses and the trade-off it makes (e.g., unit tests are fast but don't catch integration issues; chaos tests are expensive but reveal systemic weaknesses). This shows you think in terms of risk and ROI, not just tools.
Ask about the service's criticality, SLOs, compliance needs, and deployment frequency to tailor the strategy. This ensures your answer is context-aware and not generic.
Describe unit, integration, and end-to-end tests, including what each covers, typical tools (e.g., JUnit, TestContainers, Selenium), and how they fit in CI/CD. Mention the goal of fast feedback and high coverage at lower layers.
Explain canary deployments (gradual rollout to a subset of users with automated rollback) and chaos engineering (fault injection to test resilience). Highlight how they complement pre-production testing by validating in real environments.
Discuss monitoring, logging, and tracing to detect issues during canary and chaos experiments. Emphasize automated rollback and alerting to minimize impact.
Conclude by weighing cost, speed, and risk for each layer, and mention iterating on the strategy based on incident learnings and evolving business needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the vendor lock-in piece is where I had the most to say.
Start by clarifying the workload's requirements (e.g., compute, storage, messaging, data) and then map each component to equivalent managed services on GCP and AWS. Discuss portability by evaluating abstraction layers, open standards, and trade-offs between using managed services and maintaining multi-cloud flexibility.
Pro tip: Emphasize that portability is a spectrum and that the goal is to minimize lock-in where it matters most (e.g., data gravity, proprietary APIs) while leveraging managed services for productivity. Mention that Salesforce often operates in multi-cloud environments, so demonstrating awareness of both providers' strengths is key.
Ask questions to understand the workload's needs: expected scale, latency, consistency, data volume, and budget. This ensures the comparison is relevant and grounded in real constraints.
For each component (compute, storage, database, messaging, etc.), identify the closest managed services on GCP and AWS. Note differences in features, pricing models, and operational characteristics.
Evaluate each service for vendor lock-in: proprietary APIs, data formats, and migration effort. Consider using open-source or multi-cloud abstractions (e.g., Kubernetes, Terraform, Kafka) to reduce coupling.
Recommend a design that leverages managed services for productivity but isolates critical components behind abstractions or open standards. Discuss trade-offs between cost, performance, and portability.
Conclude with a clear recommendation, highlighting how it aligns with business goals and mitigates lock-in. Acknowledge that some lock-in may be acceptable for strategic benefits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.