AWS ML Engineer Associate Practice Exam
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Official Name: AWS Certified Machine Learning Engineer - Associate
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Start NowAbout the AWS ML Engineer Associate Certification
The AWS ML Engineer Associate certification is designed for professionals who develop and deploy machine learning models, automate workflows, and use IBM tools to solve real-world data challenges. As technology evolves and industry demands grow more complex; this credential validates your ability to apply real-world skills and knowledge using AWS tools and frameworks. Earning the certification positions you as a trusted expert, capable of solving high-impact challenges and contributing to secure, scalable, and efficient systems.
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Test Your Knowledge of AWS ML Engineer Associate
Question #1
The business needs to automate model training and deployment.
What is the best solution?
A) Use MLOps pipelines for continuous integration and delivery.
B) Only train models manually.
C) Disable pipeline automation.
D) Ignore deployment.
Solution
Correct answers: A – Explanation:
MLOps automates lifecycle. Manual/disabling/ignoring is slow.
Question #2
The team wants to track experiments and results.
What tool is recommended?
A) Use experiment tracking platforms integrated with code.
B) Only record results in spreadsheets.
C) Disable tracking.
D) Ignore experiments.
Solution
Correct answers: A – Explanation:
Tracking tools ensure reproducibility. Spreadsheets/disabling/ignoring is error-prone.
Question #3
The company must ensure model fairness.
What is the best practice?
A) Evaluate fairness metrics and mitigate bias in models.
B) Only test accuracy.
C) Disable fairness checks.
D) Ignore bias.
Solution
Correct answers: A – Explanation:
Fairness/bias checks are essential. Accuracy/disabling/ignoring is incomplete.
Question #4
The business wants to improve model performance.
What is the best approach?
A) Use hyperparameter tuning and cross-validation.
B) Only use default parameters.
C) Disable tuning.
D) Ignore validation.
Solution
Correct answers: A – Explanation:
Tuning/validation improves accuracy. Defaults/disabling/ignoring is limiting.
Question #5
The team needs to deploy models at scale.
What is the recommended method?
A) Use containerization and auto-scaling for model endpoints.
B) Only deploy on single server.
C) Disable scaling.
D) Ignore load.
Solution
Correct answers: A – Explanation:
Containers/scaling handle traffic. Single/disabling/ignoring is limited.
Question #6
The company must monitor models in production.
What is the best practice?
A) Implement drift detection and automated performance monitoring.
B) Only check after failures.
C) Disable monitoring.
D) Ignore drift.
Solution
Correct answers: A – Explanation:
Monitoring/drift detection catch issues early. After/disabling/ignoring is late.
Question #7
The team wants to reduce overfitting.
What should they do?
A) Apply regularization and use more diverse data.
B) Only use small datasets.
C) Disable regularization.
D) Ignore deployment.
Solution
Correct answers: A – Explanation:
Regularization/diversity prevent overfitting. Small/disabling/ignoring worsen it.
Question #8
The company wants to protect sensitive data in ML pipelines.
What is the best strategy?
A) Use data anonymization and strict access controls throughout.
B) Only anonymize some data.
C) Disable controls.
D) Ignore privacy.
Solution
Correct answers: A – Explanation:
MLOps automates lifecycle. Manual/disabling/ignoring is slow.
Question #9
The team needs to retrain models with new data.
What is the recommended workflow?
A) Automate retraining based on data triggers and monitoring results.
B) Only retrain yearly.
C) Disable retraining.
D) Ignore new data.
Solution
Correct answers: A – Explanation:
Automation/triggered retraining keeps models current. Yearly/disabling/ignoring is outdated.
Question #10
The business must explain model predictions.
What tool should they use?
A) Implement explainable AI solutions for transparency.
B) Only show outputs.
C) Disable explainability.
D) Ignore stakeholder concerns.
Solution
Correct answers: A – Explanation:
Explainable AI builds trust. Output/disabling/ignoring is opaque.
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