Which Vertex AI services track training metrics and epochs?

PMLE By islever Editorial Last updated Aug 31, 2026

Short answer

C Vertex ML Metadata, Vertex AI Experiments, and Vertex AI TensorBoard

Question

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?

Vertex ML Metadata, Vertex AI Feature Store, and Vertex AI Vizier
Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Vizier
Vertex ML Metadata, Vertex AI Experiments, and Vertex AI TensorBoard Correct Answer
Vertex AI Pipelines, Vertex AI Feature Store, and Vertex AI TensorBoard

Key Takeaways

The question tests the distinction between tools for visualizing training progress (TensorBoard) versus those for hyperparameter tuning (Vizier) or feature management (Feature Store), ensuring you select services for tracking rather than optimization.

To track model training parameters and metrics per training epoch, and to compare the performance of different model versions, you should use Vertex ML Metadata, Vertex AI Experiments, and Vertex AI TensorBoard. This combination provides the necessary infrastructure for logging, visualizing, and comparing training runs.

Common mistake: A common mistake is selecting options with Vertex AI Vizier or Vertex AI Feature Store; Vizier is used for hyperparameter tuning optimization, not logging metrics, while Feature Store is for serving features to models, not tracking training parameters.

Expert Analysis

Why the Answer Is Correct

Option C is correct because Vertex AI TensorBoard is the specific service designed to visualize metrics like loss and accuracy per training epoch. Vertex AI Experiments allows you to organize multiple training runs and compare their performance metrics side-by-side. Vertex ML Metadata underpins this process by storing the artifacts, parameters, and lineage, ensuring that all data is tracked centrally.

Why the Other Options Are Wrong

Options A and B are incorrect because they include Vertex AI Vizier, which is a service for hyperparameter tuning (optimizing parameters to find the best values) rather than simply tracking and visualizing metrics per epoch. Option D is incorrect because it includes Vertex AI Feature Store, which is used for storing and serving feature values, not for tracking training parameters or visualizing epoch-level metrics.

Community Comment Notes

The community consensus heavily favors Option C, with users explicitly noting that TensorBoard is required to track metrics "per training epoch." Comments also highlight the synergy between Vertex ML Metadata for tracking parameters and Vertex AI Experiments for managing and visualizing the results of different model versions.

Official Reference

These links open the official vendor documentation in a new tab.

Exam Strategy

Identify keywords such as "per epoch," "visualize," or "training curves" to immediately select Vertex AI TensorBoard. If the question also asks to compare different versions or runs, ensure Vertex AI Experiments is included in the solution.

Community Votes

100%

100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 6 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 6

pikachu007 👍 3 Answered 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.
winston9 👍 3 Answered C

use Tensorboard to track the model’s training parameters and the metrics per training epoch.
dija123 👍 2 Answered C

Agree with C
VipinSingla 👍 2

Why not B ?
pinimichele01 👍 1 Answered C

agree with pikachu007

Show 1 more comment

Carlose2108 👍 1 Answered C

I went C

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