More in DevOps & Cloud — page 12
Balance agility and compliance in regulated cloud
WHAT IT TESTS: compliance-aware architecture. OUTLINE: PaaS for speed where allowed, IaaS where control is required, enforced by encryption, IAM least privilege, network isolation, policy-as-code guardrails, and continuous audit logging.
Migrate an OLTP database with minimal downtime
WHAT IT TESTS: near-zero-downtime data migration. OUTLINE: take an initial bulk load, then use change data capture to replicate ongoing changes until source and target are in sync, validate, then cut over during a brief window with a rollback plan.
Design an enterprise cloud landing zone
WHAT IT TESTS: cloud foundation and governance design. OUTLINE: multi-account or subscription structure, centralized identity and SSO, network topology like hub-and-spoke, guardrails via policy and SCPs, and centralized logging.
Technical challenges of a multi-cloud strategy
WHAT IT TESTS: realism about multi-cloud complexity. OUTLINE: data consistency and egress costs across providers, cross-cloud networking and latency, and federating disparate IAM systems, plus operational and tooling overhead.
Managed services versus self-hosting trade-offs
WHAT IT TESTS: cost and control trade-offs. OUTLINE: managed services cut operational burden and speed delivery but cost more and limit control; self-hosting offers full control and tuning at the price of patching, scaling, and reliability work.
Explain the Strangler Fig pattern
WHAT IT TESTS: incremental modernization strategy. OUTLINE: a facade routes traffic, new services gradually replace legacy features one slice at a time, and the old system is retired when fully strangled.
Design a highly available web application
WHAT IT TESTS: reliability architecture. OUTLINE: redundant stateless instances across multiple zones behind a load balancer with health checks, auto-scaling, and a replicated multi-AZ datastore.
Lift-and-shift versus replatforming migration
WHAT IT TESTS: knowledge of the migration 6 Rs. OUTLINE: rehosting moves apps unchanged for speed and low risk; replatforming makes targeted optimizations for cloud benefits; explain the speed-versus-value trade-off.
Explain the Well-Architected Framework pillars
WHAT IT TESTS: foundational cloud design vocabulary. OUTLINE: name the pillars, operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability, and explain each briefly.
Design petabyte-scale distributed training
WHAT IT TESTS: end-to-end big-data ML architecture. OUTLINE: object storage with columnar formats, distributed preprocessing, a data-parallel framework with efficient sharded loading, and managed orchestration.
Design auto drift detection and retraining
WHAT IT TESTS: closed-loop MLOps design. OUTLINE: capture inputs and predictions, compute data and concept drift metrics on a schedule, alert on threshold breach, and trigger a retraining and redeploy pipeline.
How would you speed up slow single-GPU training?
WHAT IT TESTS: knowledge of scaling training. OUTLINE: vertical scaling to bigger or multi-GPU instances, then data-parallel or model-parallel distributed training across nodes.
How would you build CI/CD for an ML model?
WHAT IT TESTS: MLOps maturity beyond app deployment. OUTLINE: data and model versioning, automated training plus evaluation gates, model registry, deployment with monitoring and retraining triggers.
Inference performance bottlenecks on Lambda
WHAT IT TESTS: serverless ML serving limits. OUTLINE: cold starts loading the model, memory and CPU limits, no GPU, and package size dominate; mitigate with provisioned concurrency, loading the model once outside the handler, smaller models, and right-sized…
Deploying a real-time inference endpoint
WHAT IT TESTS: model serving operations. OUTLINE: package the model artifact and inference code in a container, choose instance type and autoscaling, configure the endpoint with health checks, and plan safe rollout like canary plus monitoring.
Feeding large object-store data into training
WHAT IT TESTS: ML data loading efficiency. OUTLINE: stream data instead of copying it all to disk, use streaming/pipe modes, shard and prefetch in parallel, and pack many small images into larger files. RED FLAG: downloading the whole 1TB to local disk first.
Pre-built AI service vs custom model
WHAT IT TESTS: buy-versus-build judgment for ML. OUTLINE: choose a managed service for speed, no ML expertise, and common tasks; build custom for domain-specific needs, control, or cost at scale. RED FLAG: always building custom when a managed API suffices.
Schema evolution without rewriting history
WHAT IT TESTS: schema evolution strategy. OUTLINE: use a table format with metadata-level evolution, add a new column rather than mutating the old, and reconcile types at read time; avoid rewriting petabytes.
The small files problem in data lakes
WHAT IT TESTS: lake performance pathology. OUTLINE: too many tiny files inflate metadata and per-file overhead, slowing queries; caused by streaming micro-batches and over-partitioning; fix with compaction and table formats like Iceberg, Delta, or Hudi.
Idempotency in data ingestion pipelines
WHAT IT TESTS: reliability under retries. OUTLINE: idempotency means re-running a step yields the same result with no duplicates; it matters because retries and at-least-once delivery are inevitable; achieve it with deduplication keys or upserts.