Machine Learning Engineer Associate · 22% of the exam

Deployment and Orchestration of ML Workflows: free practice questions

5 sample questions from our 15-question bank for this domain — answers and explanations included. These are the same scenario-based style as the real AWS exam.

1. A real-time endpoint serves a latency-critical feature but the team also needs to periodically score a huge backlog cheaply. What is the BEST combined approach?

  • A. Use a real-time endpoint for live requests and batch transform for the periodic bulk backlog✓ Correct
  • B. Use one 24/7 real-time endpoint for both
  • C. Use batch transform for the live latency-critical requests
  • D. Train a new model for each backlog record
Explanation

Match the inference option to each need: a real-time endpoint for low-latency live requests, and batch transform for cost-efficient offline bulk scoring. Forcing one endpoint for both (B) is costly/awkward, batch transform can't meet live latency (C), and per-record training (D) isn't inference.

2. An application needs low-latency, always-on predictions for each incoming user request. Which SageMaker inference option is MOST appropriate?

  • A. Real-time endpoint✓ Correct
  • B. Batch transform
  • C. Asynchronous inference for large payloads only
  • D. No deployment; run training jobs per request
Explanation

A SageMaker real-time endpoint provides persistent, low-latency inference for per-request predictions. Batch transform (B) processes datasets offline, asynchronous inference (C) suits large payloads/long processing with queuing, and running training jobs per request (D) is not an inference pattern.

3. Why containerize an ML model with a bring-your-own-container approach on SageMaker?

  • A. To package custom dependencies and runtime so training/inference is reproducible and portable across environments✓ Correct
  • B. To make the model file smaller automatically
  • C. To avoid using IAM permissions
  • D. To bypass all monitoring
Explanation

Containerization packages code, libraries, and runtime for reproducible, portable execution when built-in images don't fit your needs. It doesn't shrink model files (B), and it neither bypasses IAM (C) nor monitoring (D) — those remain in force.

4. To bring a custom training or inference environment with specific libraries, a team packages their code and dependencies into a container image. Where do they store this image for SageMaker to use?

  • A. Amazon Elastic Container Registry (Amazon ECR)✓ Correct
  • B. Amazon S3 Glacier
  • C. Amazon Route 53
  • D. AWS Secrets Manager
Explanation

Amazon ECR is the managed container registry where you push custom Docker images that SageMaker pulls for training or inference (bring-your-own-container). Glacier (B) is archival storage, Route 53 (C) is DNS, and Secrets Manager (D) stores secrets — none is a container registry.

5. Which AWS service lets you build automated, repeatable ML workflows (data processing, training, evaluation, model registration, deployment) as a pipeline?

  • A. Amazon SageMaker Pipelines✓ Correct
  • B. Amazon CloudFront
  • C. AWS Shield
  • D. Amazon SNS
Explanation

SageMaker Pipelines orchestrates end-to-end ML workflows as repeatable, automated pipelines with steps for processing, training, evaluation, and deployment. CloudFront (B), Shield (C), and SNS (D) are CDN, DDoS protection, and messaging services — not ML pipeline orchestrators.

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