Google Machine Learning Engineer Practice Exam
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Start NowAbout the Google Machine Learning Engineer Certification
The Google Machine Learning Engineer certification is designed for professionals who develop, train, and deploy machine learning models in cloud environments, using best practices to solve complex, data-driven problems. As technology evolves and industry demands grow more complex; this credential validates your ability to apply real-world skills and knowledge using Google 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 Google Machine Learning Engineer
Question #1
The company wants to put ML models into production quickly.
What is the best practice?
A) Use managed ML platforms with automated deployment pipelines.
B) Only deploy models manually.
C) Disable versioning.
D) Ignore monitoring.
Solution
Correct answers: A – Explanation:
Managed/automation speeds deployment. Manual/disabling/ignoring is slow and risky.
Question #2
The team must ensure model reproducibility.
What is the recommended solution?
A) Track experiments and use version control for code/data/models.
B) Only save final models.
C) Disable tracking tools.
D) Ignore code changes.
Solution
Correct answers: A – Explanation:
Tracking/versioning ensure reproducibility. Final-only/disabling/ignoring causes confusion.
Question #3
The business wants to monitor models for drift.
What should be implemented?
A) Set up continuous monitoring and automated retraining triggers.
B) Only check drift annually.
C) Disable monitoring.
D) Ignore retraining.
Solution
Correct answers: A – Explanation:
Continuous monitoring/retraining catch drift early. Annual/disabling/ignoring is too late.
Question #4
The company needs to protect sensitive ML data.
What is a good solution?
A) Encrypt data and restrict access using IAM policies.
B) Store data unencrypted.
C) Disable IAM.
D) Ignore data privacy.
Solution
Correct answers: A – Explanation:
Encryption/IAM secure data. Unencrypted/disabling/ignoring is unsafe.
Question #5
The team must optimize model performance.
What is the best approach?
A) Use hyperparameter tuning and scalable compute resources.
B) Only use default settings.
C) Disable tuning.
D) Ignore compute needs.
Solution
Correct answers: A – Explanation:
Tuning/scaling optimize performance. Defaults/disabling/ignoring limit results.
Question #6
The business wants to explain model predictions.
What should be done?
A) Use explainable AI tools and generate feature attribution reports.
B) Only provide raw predictions.
C) Disable explainability.
D) Ignore stakeholder questions.
Solution
Correct answers: A – Explanation:
XAI/attribution build trust. Raw/disabling/ignoring is not transparent.
Question #7
The company needs to reduce ML infrastructure costs.
What is a good strategy?
A) Use spot/preemptible instances and optimize resource allocation.
B) Always use on-demand instances.
C) Disable cost monitoring.
D) Ignore monitoring.
Solution
Correct answers: A – Explanation:
Spot/optimized reduce costs. On-demand/disabling/ignoring wastes money.
Question #8
The team must collaborate on ML projects.
What is the best practice?
A) Use shared repositories and collaborative notebooks.
B) Only work individually.
C) Disable collaboration tools.
D) Ignore team input.
Solution
Correct answers: A – Explanation:
Managed/automation speeds deployment. Manual/disabling/ignoring is slow and risky.
Question #9
The business wants to automate feature engineering.
What is the solution?
A) Use feature stores and pipelines for automated transformation.
B) Only engineer features manually.
C) Disable pipelines.
D) Ignore feature consistency.
Solution
Correct answers: A – Explanation:
Feature stores/pipelines speed engineering. Manual/disabling/ignoring is slow.
Question #10
The company must comply with ML regulations.
What should be implemented?
A) Enable model audit logging and document data lineage.
B) Only document post-incident.
C) Disable logging.
D) Ignore regulatory needs.
Solution
Correct answers: A – Explanation:
Audit/logging/lineage ensure compliance. Post-incident/disabling/ignoring is inadequate.
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