Exam Professional Machine Learning Engineer topic 1 question 241 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 241
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You have created a Vertex AI pipeline that automates custom model training. You want to add a pipeline component that enables your team to most easily collaborate when running different executions and comparing metrics both visually and programmatically. What should you do?

A. Add a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Query the table to compare different executions of the pipeline. Connect BigQuery to Looker Studio to visualize metrics.
B. Add a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Load the table into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.
C. Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Use Vertex AI Experiments to compare different executions of the pipeline. Use Vertex AI TensorBoard to visualize metrics.
D. Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Load the Vertex ML Metadata into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.

Show Suggested Answer

Suggested Answer: C 🗳️

by winston9 at Jan. 8, 2024, 5:46 p.m.

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gscharly

Highly Voted 1 year, 3 months ago

Selected Answer: C

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

upvoted 5 times

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Omi_04040

Most Recent 7 months, 4 weeks ago

Selected Answer: C

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.

upvoted 1 times

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baimus

10 months, 3 weeks ago

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.

upvoted 1 times

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fitri001

1 year, 3 months ago

Selected Answer: A

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.

upvoted 4 times