Intermediate everything in DevOps & Cloud, page 7
Configure a Kubernetes Horizontal Pod Autoscaler
HPA adjusts replica count toward a target CPU metric, needs the metrics server and pod resource requests, and scales a deployment between min and max.
When to choose bare metal over a VM
Bare metal suits latency-sensitive or high-throughput workloads needing no hypervisor overhead, single-tenant isolation for compliance, or direct hardware and licensing access.
Balance agility and compliance in regulated cloud
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.
Managed services versus self-hosting trade-offs
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
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
Redundant stateless instances across multiple zones behind a load balancer with health checks, auto-scaling, and a replicated multi-AZ datastore.
How would you speed up slow single-GPU training?
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?
Data and model versioning, automated training plus evaluation gates, model registry, deployment with monitoring and retraining triggers.
Inference performance bottlenecks on Lambda
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
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.
Idempotency in data ingestion pipelines
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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