Intermediate interview questions in Cloud Platforms, page 2
Strong versus eventual consistency in NoSQL
Strong reads see the latest write at higher latency and cost, eventual reads may be stale but are cheaper and faster, match the choice to stakes.
Data warehouse versus OLTP database
Warehouses use columnar storage for analytical scans, OLTP uses row storage for fast transactions, each fits a different workload.
Securely supplying secrets to an app
Never hardcode credentials, inject them as environment variables or pull from a secrets manager, and rotate them.
CI/CD pipeline for a container PaaS
Run tests, build the image, push the tag to a registry, then deploy it to Cloud Run. The registry is the build-to-deploy handoff.
Multi-stage Docker builds
A build stage compiles with the toolchain, the final stage uses a minimal base and copies only the artifact, cutting size and attack surface.
Exposing Kubernetes services to the internet
A Service gives stable access and LoadBalancer exposes one service, while Ingress adds L7 host and path routing with TLS for many services.
Running stateful apps with StatefulSets
Stateful apps need stable identity and storage; a StatefulSet gives stable names, ordered rollout, and per-Pod volumes.
State machines versus long-running functions
State machines externalize state, pause for human input without running compute, give built-in retries and audit history, and bill per transition.
Idempotency in event-driven systems
Idempotency means repeated processing yields the same end state; it matters because messages get redelivered; achieve it with idempotency keys and conditional writes.
Distributed tracing for serverless apps
Propagate a trace context through every hop, instrument with OpenTelemetry or X-Ray, capture spans including the database, and correlate traces with structured logs.
Centralized logging across microservices
Ship structured logs from every service into a central searchable store, then propagate a correlation ID through all hops to trace one request.
State drift in Terraform
Drift is when live infrastructure diverges from recorded state, usually via manual console changes; detect with plan or refresh, remediate by re-applying or importing.
Enforce a cloud resource compliance policy
Prevent at creation with org policies or admission checks, detect violations via continuous config scanning, and auto-remediate by stripping the IP or alerting owners.
Diagnose 100% CPU on a managed database
Correlate the spike with deploys and traffic, find top queries via the engine's views, inspect plans for missing indexes, then tune before scaling.
Rightsizing an underutilized VM fleet safely
Gather multi-week percentile metrics across CPU, memory, network and disk; pick smaller or right-family types; roll out gradually with monitoring.
Lifecycle storage tiering for compliance logs
Hot tier for 7-day query window, lifecycle rules transitioning to infrequent-access then archive, expiration at one year.
Reducing cross-region data transfer costs
Identify cross-region, cross-AZ, and internet egress; co-locate chatty components; add VPC endpoints, CDN caching, and compression.
Partitioning order events in a data lake
Partition by the columns queries filter on, typically date hierarchy and category, balancing granularity to avoid too many tiny files.
Diagnosing and fixing data skew in Spark
This is data skew, caused by uneven key distribution concentrating rows on few partitions; mitigate with salting, broadcast joins, repartitioning, or adaptive execution.
Handling late data in streaming windows
Use event-time windows with watermarks to bound lateness, allow a grace period before finalizing, and route data later than that to a side output.
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