Google Professional Machine Learning Engineer Question 449
Select 3Google Cloud PlatformYou are managing a machine learning project for a financial institution and are required to maintain model and data lineage to ensure regulatory compliance and reproducibility. Which Google Cloud solutions and practices should you implement to achieve this?
- A
Use Vertex AI Model Registry to track and version trained models.
- B
Enable BigQuery's audit logs to monitor data access and transformations.
- C
Utilize Cloud Storage versioning to store and track different versions of the raw dataset.
- D
Use Cloud Dataflow to deploy models and manage lineage automatically.
- E
Implement Artifact Registry to track the lineage of Python scripts used in the training pipeline.
Show answer and explanation
Correct answers: A, B, C
Explanation
Maintaining model and data lineage is critical in regulated industries like finance. Vertex AI Model Registry helps with tracking trained models, while BigQuery's audit logs and Cloud Storage versioning aid in monitoring data access and changes. These tools together provide a comprehensive approach to lineage management. Cloud Dataflow and Artifact Registry, while useful for other tasks, are not specifically tailored for lineage tracking in this context.
- A. Correct.
Correct: Vertex AI Model Registry is specifically designed to track and version trained models, which is essential for maintaining model lineage.
- B. Correct.
Correct: BigQuery's audit logs provide detailed records of data access and transformations, making them crucial for tracking data lineage.
- C. Correct.
Correct: Cloud Storage versioning facilitates tracking changes to raw datasets, which is an important aspect of maintaining data lineage.
- D. Incorrect.
Incorrect: While Cloud Dataflow is a powerful tool for data processing, it does not inherently manage model or data lineage. Additional tools or configurations would be needed.
- E. Incorrect.
Incorrect: Artifact Registry is primarily designed for managing and securing container images and artifacts, not specifically for model and data lineage.