Examtopics

コメント(8)

• 正解だと思う選択肢: D

  Why not the others? A. Dataplex & Vertex AI Feature Store: While Dataplex can manage data across projects, it's not directly tied to model versioning and comparison. Feature Store focuses on feature engineering, not model version management. B. Vertex AI Feature Store & Vertex AI TensorBoard: Similar to option A, Feature Store isn't directly involved in model version tracking, and TensorBoard is primarily for visualizing training data and metrics, not model version comparison across projects. C. Dataplex & Vertex AI ML Metadata: Dataplex, as mentioned earlier, doesn't directly address model version comparison. While ML Metadata tracks lineage, it might not have the experiment management features of Vertex AI Experiments.

  👍 5

  fitri0012024/04/17
• 正解だと思う選択肢: C

  I go with C. Dataplex to centralize different Google projects. Vertex AI experiments + ML Metadata to track experiment lineage, parameter usage etc and compare models.

  👍 3

  b1a8fae2024/01/17
• 正解だと思う選択肢: B

  My Answer: B Vertex AI Pipelines: to create, deploy, and manage ML pipelines, which are essential for orchestrating your ML workflow, especially when dealing with data spanning multiple projects. Vertex AI Feature Store: It's crucial for managing feature data across different projects. Vertex AI Experiments: track and compare the performance of different versions of your models, enabling you to experiment

  Why not the other: Dataplex: not specifically tailored for managing ML workflows or model training. Vertex AI ML metadata: not sufficient on its own to cover all aspects of managing the ML workflow across multiple projects. Vertex AI TensorBoard: not specifically designed for managing the end-to-end ML workflow or tracking model versions across multiple projects.

  👍 2

  guilhermebutzke2024/02/18