InterviewCloud
AWS scenarios for data engineering, MLOps, Generative AI systems, deployment, monitoring, and certification planning.
Use a containerized model endpoint with CI/CD, monitoring, autoscaling, and rollback.
A strong answer mentions SageMaker endpoints or ECS/EKS, model artifacts in S3, image registry in ECR, traffic shifting, CloudWatch metrics, alarms, and model/data drift checks.
Separate raw, curated, and serving layers with cataloging, partitioning, security, and quality checks.
Use S3 as storage, Glue Data Catalog for metadata, IAM and Lake Formation for access, Athena or Spark for querying, and lifecycle policies for cost control.
Ingest documents into S3, process chunks, embed them, store vectors, retrieve context, and generate with Bedrock.
Typical pieces include S3, Lambda or Step Functions, Bedrock embeddings and generation, OpenSearch Serverless or a vector DB, IAM boundaries, citations, evaluation, and CloudWatch tracing.
Use Lambda for short event-driven tasks and ECS for long-running services, custom runtimes, or steady workloads.
Interviewers expect tradeoffs around cold starts, runtime limits, cost profile, operational control, networking, scaling, and deployment complexity.
Cloud Practitioner is optional; Solutions Architect Associate and ML/AI-focused certifications are more useful for interview depth.
Treat certifications as structure, not proof. For interviews, prioritize architecture scenarios, IAM, networking, storage, monitoring, cost, and hands-on projects.