AWS Interview Questions for 2 Years Experience

Key AI/ML services: (1) SageMaker — fully managed ML platform for training and deployment, (2) EC2 with GPU instances (p3, g4dn, g5) — for custom training, (3) S3 — for storing datasets and model artifacts, (4) Lambda — for serverless inference on small models, (5) ECS/EKS — for containerized AI API deployments, (6) Bedrock — managed LLM APIs (Claude, Llama), (7) OpenSearch — for vector search at scale.

IAM roles define permissions for AWS services. For AI apps: (1) Never use root account credentials, (2) Give EC2/ECS tasks an IAM role with only needed permissions (least privilege), (3) For SageMaker: role needs S3 read/write + CloudWatch logs, (4) Use IAM policies to restrict which S3 buckets, Secrets Manager secrets, and Bedrock models each service can access. IAM mistakes cause security breaches — always review with IAM Access Analyzer.

S3 is object storage with 99.999999999% durability. For ML: store raw datasets, processed features, trained model weights, and predictions. Best practices: (1) Use versioning for model artifacts, (2) Lifecycle policies to move old models to Glacier (cheaper), (3) S3 Transfer Acceleration for large dataset uploads, (4) VPC endpoints to avoid egress costs, (5) S3 Select to query large CSV/Parquet files without downloading. For cost: use intelligent tiering for infrequently accessed model weights.