Machine Learning Engineer Associate · 26% of the exam
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 regression model predicts house prices. Which metric expresses error in the SAME units as the target and penalizes large errors more heavily?
RMSE is in the target's units and squares errors, penalizing large deviations more — a standard regression metric. Precision, recall, and F1 (B, C, D) are classification metrics and don't apply to predicting a continuous price.
2. 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?
SageMaker Experiments organizes, tracks, and compares training runs (parameters, metrics, artifacts) for reproducibility. Edge Manager (B) manages edge deployments, Glue (C) is ETL, and CloudFront (D) is a CDN — none tracks experiments.
3. For a structured tabular classification/regression problem on SageMaker where you want strong performance with minimal deep-learning setup, which built-in algorithm is a common strong choice?
XGBoost (a gradient-boosted tree algorithm, available as a SageMaker built-in) is a go-to for tabular classification/regression with strong results and modest tuning. A CNN (B) targets images, speech-to-text (C) targets audio, and a DNS resolver (D) is not an ML algorithm.
4. Which SageMaker capability automatically searches hyperparameter combinations to find the best-performing model configuration?
SageMaker Automatic Model Tuning runs training jobs across hyperparameter ranges (using strategies like Bayesian search) to find the best configuration. Model Monitor (B) watches production, Ground Truth (C) labels data, and Feature Store (D) manages features — none tunes hyperparameters.
5. Which metric balances precision and recall into a single score, useful when you care about both false positives and false negatives?
The F1 score is the harmonic mean of precision and recall, balancing both — valuable with class imbalance. R-squared (B) is a regression metric, accuracy (C) ignores the precision/recall tradeoff, and log loss (D) measures probability calibration, not the precision-recall balance directly.
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