📋 Model Deployment Cheat Sheet

Deployment questions usually hinge on latency, throughput, payload size, cost, and whether predictions are online or offline.

Why This Cheat Sheet Matters for MLA-C01

This cheat sheet covers the most important ML Model Deployment concepts tested on the MLA-C01 (AWS Machine Learning Engineer Associate) certification exam. It contains 2 sections with 8 key points that you should memorize before exam day. Review real-time endpoints, serverless inference, asynchronous inference, batch transform, endpoint variants, autoscaling, and deployment patterns. Use this as a quick-reference guide during your final review sessions.

2Sections
8Key Points

Inference Options

  • Real-time endpoints serve low-latency online predictions.
  • Serverless inference reduces idle cost for intermittent traffic.
  • Asynchronous inference supports larger payloads and longer processing times.
  • Batch transform scores stored datasets without a persistent endpoint.

Production Controls

  • Use endpoint variants for traffic shifting or A/B testing.
  • Configure autoscaling from endpoint metrics to handle traffic changes.
  • Keep approved model versions in Model Registry before production deployment.
  • Monitor endpoint invocation metrics and model quality after release.

Practice Model Deployment Questions

Put your knowledge to the test with practice questions.

More MLA-C01 Cheat Sheets