According to the official exam guide1, one of the skills assessed in the exam is to “track the lineage

of pipeline artifacts”. Vertex ML Metadata2 is a service that allows you to store, query, and visualize

metadata associated with your ML workflows, such as datasets, models, metrics, and executions.

Vertex ML Metadata helps you track the provenance and lineage of your ML artifacts and understand

the relationships between them. Vertex AI Experiments3 is a service that allows you to track and

compare the results of your model training runs. Vertex AI Experiments automatically logs metadata

such as hyperparameters, metrics, and artifacts for each training run. You can use Vertex AI

Experiments to train your custom model using TensorFlow, PyTorch, XGBoost, or scikit-learn. Vertex

AI TensorBoard4 is a service that allows you to visualize and monitor your ML experiments using

TensorBoard, an open source tool for ML visualization. Vertex AI TensorBoard helps you track the

model’s training parameters and the metrics per training epoch, and compare the performance of

each version of the model. Therefore, option C is the best way to determine which Vertex AI services

to include in the workflow for the given use case. The other options are not relevant or optimal for

this scenario. Reference:

Professional ML Engineer Exam Guide

Vertex ML Metadata

Vertex AI Experiments

Vertex AI TensorBoard

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions