Google Professional Machine Learning Engineer Pmle Practice Test - Set 1
Test your knowledge with this Google Professional Machine Learning Engineer Pmle mock exam. Get real-world IT questions and prepare for certification success.
Google Professional Machine Learning Engineer (PMLE) - Exam Information
Exam Information
Exam Code
Google Professional Machine Learning Engineer Pmle
Exam Title
Google Professional Machine Learning Engineer (PMLE)
Vendor
Google
Difficulty
Advanced
Duration
120 Minutes
Question Format
Multiple Choice
Last Updated
March 12, 2025
Tests expertise in building, training, and deploying ML models on Google Cloud.
1. Which Google Cloud service is used for building and training machine learning models at scale?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
2. Which Google Cloud service is used for deploying machine learning models?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
3. Which Google Cloud service is used for managing machine learning pipelines?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
4. Which Google Cloud service is used for managing machine learning datasets?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
5. Which Google Cloud service is used for managing machine learning experiments?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
6. Which Google Cloud service is used for managing machine learning model versions?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
7. Which Google Cloud service is used for managing machine learning model monitoring?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
8. Which Google Cloud service is used for managing machine learning model deployment?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
9. Which Google Cloud service is used for managing machine learning model training?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
10. Which Google Cloud service is used for managing machine learning model evaluation?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
11. Which Google Cloud service is used for managing machine learning model prediction?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
12. Which Google Cloud service is used for managing machine learning model serving?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
13. Which Google Cloud service is used for managing machine learning model optimization?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
14. Which Google Cloud service is used for managing machine learning model debugging?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
15. Which Google Cloud service is used for managing machine learning model logging?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
16. Which Google Cloud service is used for managing machine learning model monitoring?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
17. Which Google Cloud service is used for managing machine learning model versioning?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
18. Which Google Cloud service is used for managing machine learning model deployment?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
19. Which Google Cloud service is used for managing machine learning model training?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
20. Which Google Cloud service is used for managing machine learning model evaluation?
Cloud AI Platform
BigQuery ML
Cloud SQL
Dataflow
✅ Correct Answer: Cloud AI Platform
21. Which Vertex AI feature automatically selects the best model architecture for your data?
AutoML
Custom Training
Pipeline Jobs
Feature Store
✅ Correct Answer: AutoML
22. What is the primary advantage of using Vertex AI Feature Store?
Centralized feature management and reuse
Lower training costs
Faster model deployment
Better visualization
✅ Correct Answer: Centralized feature management and reuse
23. Which service would you use to deploy ML models with automatic scaling?
Vertex AI Endpoints
Cloud Functions
Cloud Run
App Engine
✅ Correct Answer: Vertex AI Endpoints
24. What is the purpose of Vertex AI Pipelines?
Orchestrate ML workflows
Store training data
Visualize model results
Monitor network traffic
✅ Correct Answer: Orchestrate ML workflows
25. Which tool is used for hyperparameter tuning in Vertex AI?
Vertex AI Vizier
Vertex AI Feature Store
Vertex AI Workbench
Vertex AI TensorBoard
✅ Correct Answer: Vertex AI Vizier
26. What is the primary advantage of using BigQuery ML?
Build ML models using SQL
Lower storage costs
Faster training times
Better visualization
✅ Correct Answer: Build ML models using SQL
27. Which Vertex AI component provides managed Jupyter notebooks?
Vertex AI Workbench
Vertex AI Training
Vertex AI Prediction
Vertex AI Pipelines
✅ Correct Answer: Vertex AI Workbench
28. What is the purpose of Vertex AI Model Monitoring?
Detect model drift and performance issues
Optimize hyperparameters
Store training data
Visualize feature importance
✅ Correct Answer: Detect model drift and performance issues
29. Which service is best for training custom TensorFlow models at scale?
Vertex AI Custom Training
AutoML
BigQuery ML
Dataproc
✅ Correct Answer: Vertex AI Custom Training
30. What is the primary advantage of using Vertex AI over standalone AI Platform?
Unified ML platform with more features
Lower cost
Faster training
Better Python support
✅ Correct Answer: Unified ML platform with more features
31. Which Vertex AI feature helps explain model predictions?
Vertex AI Explanations
Vertex AI Feature Store
Vertex AI Pipelines
Vertex AI Vizier
✅ Correct Answer: Vertex AI Explanations
32. What is the purpose of Vertex AI TensorBoard?
Visualize ML experiments
Store training data
Deploy models
Monitor predictions
✅ Correct Answer: Visualize ML experiments
33. Which service is best for batch predictions on large datasets?
Vertex AI Batch Prediction
Vertex AI Online Prediction
Cloud Functions
Dataflow
✅ Correct Answer: Vertex AI Batch Prediction
34. What is the primary advantage of using Vertex AI Feature Store?
Feature sharing and consistency across projects
Lower storage costs
Faster model training
Better visualization
✅ Correct Answer: Feature sharing and consistency across projects
35. Which Vertex AI component manages the complete ML lifecycle?
Vertex AI Unified Platform
Vertex AI Workbench
Vertex AI Training
Vertex AI Prediction
✅ Correct Answer: Vertex AI Unified Platform
36. What is the purpose of Vertex AI Matching Engine?
Vector similarity search
Hyperparameter tuning
Feature engineering
Model deployment
✅ Correct Answer: Vector similarity search
37. Which service is best for deploying low-latency ML models?
Vertex AI Online Prediction
Vertex AI Batch Prediction
Cloud Functions
Cloud Run
✅ Correct Answer: Vertex AI Online Prediction
38. What is the primary advantage of using AutoML over custom models?
No ML expertise required
Lower cost
Faster training
Better accuracy
✅ Correct Answer: No ML expertise required
39. Which Vertex AI feature helps track ML experiments?
Vertex AI Experiments
Vertex AI Pipelines
Vertex AI Feature Store
Vertex AI TensorBoard
✅ Correct Answer: Vertex AI Experiments
40. What is the purpose of Vertex AI Model Registry?
Manage and version ML models
Store training data
Visualize predictions
Monitor infrastructure
✅ Correct Answer: Manage and version ML models
41. Which service is best for processing unstructured data for ML?
Vertex AI Data Labeling
BigQuery
Cloud Storage
Dataflow
✅ Correct Answer: Vertex AI Data Labeling
42. What is the primary advantage of using Vertex AI Pipelines?
Reproducible ML workflows
Lower training costs
Faster predictions
Better visualization
✅ Correct Answer: Reproducible ML workflows
43. Which Vertex AI component provides pre-trained APIs for common tasks?
Vertex AI Pre-trained APIs
Vertex AI Custom Training
Vertex AI Workbench
Vertex AI Feature Store
✅ Correct Answer: Vertex AI Pre-trained APIs
44. What is the purpose of Vertex AI Notebooks?
Interactive development environment
Model deployment
Feature engineering
Data storage
✅ Correct Answer: Interactive development environment
45. Which service is best for building recommendation systems?
Vertex AI Recommendations AI
AutoML Tables
BigQuery ML
Custom Training
✅ Correct Answer: Vertex AI Recommendations AI
46. What is the primary advantage of using BigQuery ML for classification?
No data movement required
Better accuracy
Faster training
More algorithm choices
✅ Correct Answer: No data movement required
47. Which Vertex AI feature helps optimize model hyperparameters?
Vertex AI Vizier
Vertex AI TensorBoard
Vertex AI Feature Store
Vertex AI Pipelines
✅ Correct Answer: Vertex AI Vizier
48. What is the purpose of Vertex AI Model Deployment?
Serve predictions at scale
Store training data
Label datasets
Monitor infrastructure
✅ Correct Answer: Serve predictions at scale
49. Which service is best for processing natural language?
Vertex AI Natural Language API
AutoML Vision
Recommendations AI
Video Intelligence API
✅ Correct Answer: Vertex AI Natural Language API
50. What is the primary advantage of using Vertex AI over building your own infrastructure?
Managed service handles scaling and maintenance
Lower cost
More customization
Better performance
✅ Correct Answer: Managed service handles scaling and maintenance
The Google Professional Machine Learning Engineer Pmle certification is a globally recognized credential for IT professionals.
This practice test helps you prepare by covering key topics like hardware, networking, troubleshooting, and security.
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