AWS Certified Machine Learning Engineer - Associate MLA-C01
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Free AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions
Question 39 A data scientist needs to evaluate classification model performance beyond accuracy. What metrics provide comprehensive evaluation?
- A) Accuracy only
- B) Precision, recall, F1-score, AUC-ROC, confusion matrix
- C) Training loss only
- D) No other metrics needed
Explanation
Classification metrics: (1) Confusion matrix—TP, TN, FP, FN counts, (2)
Precision—TP/(TP+FP), minimizes false positives, (3) Recall (sensitivity)—TP/(TP+FN),
minimizes false negatives, (4) F1-score—harmonic mean of precision and recall, (5)
AUC-ROC—area under ROC curve, threshold-independent. Accuracy misleading for
imbalanced datasets. Choose based on: cost of FP vs FN, class imbalance. SageMaker Model
Monitor tracks metrics. Use for: model selection, threshold tuning, performance monitoring.
Correct Answer
B) Precision, recall, F1-score, AUC-ROC, confusion matrix
A data scientist needs to evaluate model performance on a small test set reliably. What technique provides robust evaluation?
- A) Single train-test split
- B) Bootstrap confidence intervals or repeated cross-validation
- C) Test set only
- D) Training accuracy
Explanation
Robust evaluation with small data:
(1) Bootstrap—resample test set with replacement, compute metrics, generate confidence intervals,
(2) Repeated cross-validation—multiple K-fold runs with different splits, average results,
(3) Stratified sampling—ensures representative test set,
(4) Statistical tests—comparing models with McNemar's test, Wilcoxon signed-rank.
Report: mean and confidence intervals/standard deviation.
Use for: assessing uncertainty, model comparison, small datasets.
Avoid: data leakage, peeking at test set during development.
Multiple metrics for comprehensive evaluation.
A larger test set reduces uncertainty.
Correct Answer Is:
B) Bootstrap confidence intervals or repeated cross-validation
Question 68 An ML pipeline requires automated testing of model quality before deployment. What validation step prevents poor models from deploying?
- A) Deploy without testing
- B) Conditional deployment in Pipeline based on model metrics thresholds
- C) No quality checks
- D) Manual approval only
Explanation
Automated model validation: (1) Evaluation step in SageMaker Pipeline computes metrics on
test set, (2) Condition step compares metrics to threshold (accuracy > 0.85, AUC > 0.90), (3)
Deploy to production if condition met, else fail pipeline or route to manual review. Model quality
monitoring compares to baseline. A/B testing compares to the current production model. Use for:
preventing model degradation, automated MLOps, quality assurance. Register successful models
to Model Registry with metrics. Alert on quality issues.
Correct Answer
B) Conditional deployment in Pipeline based on model metrics thresholds
Question 15 A data scientist needs to visualize model training metrics in real-time. What tool provides an interactive ML development environment?
- A) Jupyter on EC2 manually
- B) Local IDE only
- C) SageMaker Studio providing integrated ML development environment
- D) CloudWatch dashboards only
Explanation
SageMaker Studio: web-based IDE for ML development. Integrated tools: notebooks,
experiments, model registry, pipelines, feature store, model monitor. Real-time training metrics
visualization. Debugger profiling and debugging. Compare experiments side-by-side. One-click
model deployment. Team collaboration with shared workspaces. SageMaker Studio Lab for free
experimentation. Use for: end-to-end ML workflow, experiment tracking, model comparison.
Single sign-on with IAM or SSO.
Correct Answer
C) SageMaker Studio providing integrated ML development environment
A machine learning pipeline requires automated data quality validation. What checks ensure data quality?
- A) Schema validation, statistical tests, completeness checks, and consistency rules
- B) No validation
- C) Visual inspection only
- D) Trust data blindly
Explanation
Data quality validation:
(1) Schema validation—data types, required columns, value ranges,
(2) Statistical tests—distribution checks (mean, std within bounds), outlier detection,
(3) Completeness—missing value thresholds, required fields,
(4) Consistency—cross-field validation (start_date < end_date), referential integrity,
(5) Uniqueness—duplicate detection,
(6) Freshness—data recency checks.
Tools: Great Expectations, TFDV, AWS Glue Data Quality.
Implement in SageMaker Processing job.
Fail pipeline if validation fails.
Use for: preventing bad data training, monitoring data sources.
Generate data quality reports.
Alert on quality degradation.
Correct Answer Is:
A) Schema validation, statistical tests, completeness checks, and consistency rules
An ML application requires efficient storage of embeddings for similarity search. What service provides vector database capabilities?
- A) Standard RDS
- B) DynamoDB
- C) S3 only
- D) OpenSearch with k-NN plugin or specialized vector databases
Explanation
Vector storage for similarity search:
(1) OpenSearch k-NN plugin—approximate nearest neighbor search using HNSW, product quantization,
(2) Specialized vector databases—Pinecone, Weaviate, Milvus,
(3) FAISS—Facebook AI Similarity Search library in memory/disk.
Use cases: semantic search, recommendation systems, duplicate detection.
Operations: index embeddings, query for similar vectors.
Approximate methods trade accuracy for speed (millions of vectors).
Exact search is expensive at scale.
Consider: vector dimensionality, dataset size, query latency.
Integration: embed with SageMaker, store/search in OpenSearch.
Correct Answer Is:
D) OpenSearch with k-NN plugin or specialized vector databases
Question 57 A machine learning engineer needs to detect concept drift in a production model. What monitoring approach identifies drift?
- A) No drift monitoring
- B) Monitor input feature distributions and prediction distributions over time
- C) Model accuracy only
- D) Single-time evaluation
Explanation
Concept drift detection: (1) Feature drift—monitor input feature distributions comparing to
training baseline using statistical tests (KS test, chi-squared, PSI), (2) Prediction drift—monitor
prediction distribution changes, (3) Ground truth drift—compare predictions to actual labels
(requires label feedback), (4) Model quality drift—track performance metrics over time.
SageMaker Model Monitor automates drift detection. Set thresholds for violations. Alert when
drift detected. Retrain model when drift is significant. Use for: maintaining production model
accuracy, adaptive ML systems.
Correct Answer
B) Monitor input feature distributions and prediction distributions over time
A machine learning pipeline requires consistent preprocessing between training and inference. What pattern ensures consistency?
- A) Separate preprocessing code
- B) Serialize preprocessing pipeline with model or use inference pipelines
- C) Manual preprocessing at inference
- D) Different preprocessing
Explanation
Preprocessing consistency:
(1) SKLearn Pipeline—chains transformers and model, single object for fit/predict, serialized together,
(2) TensorFlow SavedModel—includes preprocessing layers,
(3) SageMaker inference pipeline—chains preprocessing and model containers,
(4) Feature Store—consistent feature computation,
(5) Model artifacts include preprocessing.
Benefits: eliminates train-serve skew, simplifies deployment, reduces errors.
Use for: scaling, encoding, feature engineering.
Test: validate preprocessing outputs match between training and inference.
Version preprocessing with model.
Correct Answer Is:
B) Serialize preprocessing pipeline with model or use inference pipelines
A machine learning pipeline requires handling data versioning for reproducibility. What practice tracks dataset versions?
- A) Overwrite datasets
- B) No versioning
- C) S3 versioning, dataset naming conventions, or DVC for dataset version control
- D) Manual tracking
Explanation
Dataset versioning:
(1) S3 versioning—automatic version tracking, retrieve any historical version,
(2) Naming conventions—include version/timestamp in S3 prefix (datasets/v1.0/, datasets/2024-01-15/),
(3) DVC (Data Version Control)—Git-like versioning for data, metadata in Git, data in S3,
(4) Dataset registry—catalog with metadata (version, schema, lineage),
(5) Immutable datasets—never modify, create new versions.
Track: data sources, transformations, statistics.
Link: dataset version to experiment/model.
Use for: reproducibility, debugging, compliance.
SageMaker Experiments tracks dataset metadata.
Benefits: reproduce results, audit trail, identify data issues.
Correct Answer Is:
C) S3 versioning, dataset naming conventions, or DVC for dataset version control
A data scientist needs to reduce training time for large dataset. What data sampling strategy balances speed and performance?
- A) Train on entire dataset always
- B) Random sampling, importance sampling, or active learning
- C) Use single sample
- D) No sampling
Explanation
Data sampling strategies:
(1) Random sampling—simple, unbiased, may miss rare cases,
(2) Stratified sampling—maintains class distribution,
(3) Importance sampling—weight samples by importance,
(4) Active learning—iteratively select most informative samples,
(5) Curriculum learning—start with easy samples, progress to hard.
Use for: prototyping, large datasets, class imbalance.
Monitor: performance on full dataset vs sample.
Start with sample, validate on full data.
SageMaker Pipe mode enables training on subset.
Balance: training time vs model quality.
Correct Answer Is:
B) Random sampling, importance sampling, or active learning
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