AWS Machine Learning Engineer Associate (MLA-C01) · 26% practice allocation

AWS MLA-C01: ML Model Development Practice Questions

5 free sample questions from the ML Model Development domain, with answers and explanations. The full MLA-C01 bank has 59 questions across 4 domains. Questions are scenario-based and written from the official AWS exam guide.

1.

A team wants to validate model performance more robustly than a single train/validation split, especially with limited data. Which technique should they use?

  • A. K-fold cross-validation✓ Correct
  • B. Training on 100% of the data with no evaluation
  • C. Only measuring training accuracy
  • D. Deploying without any validation
Explanation

K-fold cross-validation splits data into k folds, training and validating across rotations to get a more reliable performance estimate — valuable with limited data. The other options skip proper evaluation, risking overfitting and unknown real-world performance.

2.

Which metric balances precision and recall into a single score, useful when you care about both false positives and false negatives?

  • A. R-squared
  • B. Log loss only
  • C. Accuracy
  • D. F1 score✓ Correct
Explanation

The F1 score is the harmonic mean of precision and recall, balancing both — valuable with class imbalance. R-squared (A) is a regression metric, accuracy (C) ignores the precision/recall tradeoff, and log loss (B) measures probability calibration, not the precision-recall balance directly.

3.

A spam filter must minimize FALSE POSITIVES (legitimate emails wrongly flagged as spam). Which metric is MOST important to maximize?

  • A. Precision✓ Correct
  • B. Recall
  • C. RMSE
  • D. Mean absolute error
Explanation

Precision measures the fraction of predicted positives that are truly positive; maximizing it reduces false positives (good emails flagged as spam). Recall (B) targets missed positives, and RMSE/MAE (C, D) are regression metrics, not applicable to this classification concern.

4.

Which SageMaker capability lets you quickly access and deploy prebuilt models and foundation models to accelerate development?

  • A. Amazon Macie
  • B. Amazon SageMaker Model Monitor
  • C. AWS CloudTrail
  • D. Amazon SageMaker JumpStart✓ Correct
Explanation

SageMaker JumpStart provides prebuilt models, foundation models, and solution templates you can deploy or fine-tune quickly. Model Monitor (B) is for production monitoring, CloudTrail (C) logs API activity, and Macie (A) discovers sensitive data — none is the prebuilt-model hub.

5.

A team wants to track and compare metrics, parameters, and artifacts across many training runs to reproduce the best model. Which SageMaker capability supports this?

  • A. Amazon SageMaker Experiments✓ Correct
  • B. AWS Glue
  • C. Amazon SageMaker Edge Manager
  • D. Amazon CloudFront
Explanation

SageMaker Experiments organizes, tracks, and compares training runs (parameters, metrics, artifacts) for reproducibility. Edge Manager (C) manages edge deployments, Glue (B) is ETL, and CloudFront (D) is a CDN — none tracks experiments.

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