Cloud
189 bites tagged Cloud: interview questions with model answers, and 60-second explainers.
Test yourself: Top 30 Cloud interview questions →Multiple choice, with the correct answer and why it is correct on every question. Free, no sign-in.AWS Bedrock versus a direct provider API
Bedrock unifies many models with IAM, VPC, and cloud integration; a direct provider API gives earliest models, full feature parity, and simpler vendor terms.
Managed RDS vs self-managed DB on EC2
Managed RDS offloads patching, backups, failover, and replication, freeing the team to build product; self-managed EC2 means you own all that toil.
Why separate storage and compute in a cloud warehouse?
Data lives in cheap shared object storage while independent compute clusters scale separately, enabling elastic, concurrent, isolated workloads and pay-per-use.
Design a multi-tenant model serving platform
Share infrastructure to cut cost while enforcing tenant data isolation, fair resource allocation against noisy neighbors, and per-tenant performance via quotas and autoscaling.
Optimize cost of a big-data analytics platform
Storage tiering and lifecycle plus compression and partitioning; compute via spot instances, right-sizing, and efficient file formats; query and pipeline optimization to scan less data.
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.
Migrate an OLTP database with minimal downtime
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.
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.
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.
Design petabyte-scale distributed training
Object storage with columnar formats, distributed preprocessing, a data-parallel framework with efficient sharded loading, and managed orchestration.
Design auto drift detection and retraining
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?
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.
Feeding large object-store data into training
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.
Pre-built AI service vs custom model
Choose a managed service for speed, no ML expertise, and common tasks; build custom for domain-specific needs, control, or cost at scale.
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.
CSV vs JSON vs Parquet for analytics
CSV and JSON are row-based, human-readable, and bulky; columnar Parquet/ORC compress well and read only needed columns; choose columnar for analytics.
ETL versus ELT in cloud data platforms
ETL transforms before loading into the target; ELT loads raw first then transforms in the warehouse, leveraging cheap storage and elastic compute.
Data lake versus data warehouse
Lakes store raw, schema-on-read data of any type cheaply; warehouses store curated, schema-on-write structured data for fast SQL; choose a lake for varied raw data and ML.
Resilient stateful batch on Spot Instances
Externalize state and checkpoint to durable storage, react to interruption and rebalance notices to drain gracefully, diversify instance pools.
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