PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #97
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #97: Real Exam Question with Answer & Explanation
The correct answer is D: Manage your ML workflows with Vertex ML Metadata.. Vertex ML Metadata is Google Cloud's purpose-built service for tracking ML metadata: experiments, pipeline runs, input/output artifacts, and their lineage. It enables reproducible experiments by recording exactly what data, parameters, and code produced each model artifact. The o
Question
You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?
Options
- AStore your tf.logging data in BigQuery.
- BManage all relational entities in the Hive Metastore.
- CStore all ML metadata in Google Cloud's operations suite.
- DManage your ML workflows with Vertex ML Metadata.
Explanation
Vertex ML Metadata is Google Cloud's purpose-built service for tracking ML metadata: experiments, pipeline runs, input/output artifacts, and their lineage. It enables reproducible experiments by recording exactly what data, parameters, and code produced each model artifact. The other options serve different purposes: BigQuery (A) is an analytical data warehouse; Hive Metastore (B) tracks schema/table metadata for Hadoop-ecosystem data warehouses; Cloud Operations (C) is an infrastructure monitoring and logging suite. None of those are designed to track ML-specific metadata like feature sets, model versions, or training run parameters.
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