Exam Professional Machine Learning Engineer topic 1 question 241 discussion

went with C. Experiments can be used to compare executions and metrics

Vertex ML Metadata and Vertex AI Experiments provide APIs and SDKs that allow you to access and analyze metrics programmatically. This enables you to automate comparisons, generate reports, or perform custom analysis on your pipeline executions.

I can see why C is tempting, but Vertex Experiment's isn't actually required here, just a nice to have, whereas Workbench is actually required as they say "visually AND programatically". It's literally the only answer that allows programmatic comparison of the data in the metadata store.

Why A? BigQuery: Stores pipeline metrics from different executions in a central location, allowing easy access for team members. BigQuery Queries: Enables programmatic comparison of metrics across runs using SQL queries. Looker Studio: Provides a collaborative visualization platform for team members to explore and compare metrics visually. why not C? Vertex AI Experiments and TensorBoard: While Vertex AI Experiments can leverage ML Metadata for lineage tracking, it's not ideal for general metric comparison. TensorBoard is primarily for visualizing training data during the pipeline execution, not comparing results across runs.

Clearly C.

C is the correct one here