Exam Professional Machine Learning Engineer topic 1 question 203 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 203
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You work for a bank. You have been asked to develop an ML model that will support loan application decisions. You need to determine which Vertex AI services to include in the workflow. You want to track the model’s training parameters and the metrics per training epoch. You plan to compare the performance of each version of the model to determine the best model based on your chosen metrics. Which Vertex AI services should you use?

A. Vertex ML Metadata, Vertex AI Feature Store, and Vertex AI Vizier
B. Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Vizier
C. Vertex ML Metadata, Vertex AI Experiments, and Vertex AI TensorBoard
D. Vertex AI Pipelines, Vertex AI Feature Store, and Vertex AI TensorBoard

Show Suggested Answer

Suggested Answer: C 🗳️

by pikachu007 at Jan. 13, 2024, 4:31 a.m.

Comments

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dija123

11 months, 3 weeks ago

Selected Answer: C

Agree with C

upvoted 2 times

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pinimichele01

1 year, 2 months ago

Selected Answer: C

agree with pikachu007

upvoted 1 times

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VipinSingla

1 year, 3 months ago

Why not B ?

upvoted 2 times

info_appsatori

10 months, 1 week ago

I guess because Vizier is a tool that helps to tune hyperparameters, and in a contrary Tensorboard is a tool to explore experiments.

upvoted 2 times

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Carlose2108

1 year, 3 months ago

Selected Answer: C

I went C

upvoted 1 times

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winston9

1 year, 5 months ago

Selected Answer: C

use Tensorboard to track the model’s training parameters and the metrics per training epoch.

upvoted 3 times

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pikachu007

1 year, 5 months ago

Selected Answer: C

Vertex ML Metadata: Tracks model training parameters, hyperparameters, metrics, and lineage information. Stores metadata in a central repository for easy access and comparison. Integrates seamlessly with Vertex AI Experiments and TensorBoard. Vertex AI Experiments: Organizes and manages model training runs as experiments. Visualizes experiment results, including metrics and parameter comparisons. Facilitates tracking of the best performing model versions. Vertex AI TensorBoard: Provides detailed visualizations of training metrics and model performance. Enables analysis of model behavior at each training epoch. Integrates with Vertex AI Experiments for seamless access to experiment data.

upvoted 3 times

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