A media company wants to train a video classification model. The videos are stored in Cloud Storage, and metadata is in BigQuery. They want to use a custom training job on Vertex AI. What is the recommended way to ingest the video data into the training pipeline?

Convert the videos to base64 and store them in BigQuery for training.

Copy the videos to a local data center and then upload them to the training job.

Read the video files from Cloud Storage using the file system, and use the BigQuery API for metadata.

Use the BigQuery Storage API to read the video files directly from BigQuery.

A data engineer needs to store a large dataset of labeled images for a computer vision training pipeline. Which Google Cloud service is most appropriate for storing the image files and their labels in a format that integrates well with ML training?

Cloud Storage for both the image files and the labels

Cloud SQL for the image files and Cloud Storage for the labels

Cloud Storage for the image files and BigQuery for the labels

BigQuery for both the image files and the labels

In which scenario is fine-tuning a foundational model most appropriate?

When you want to deploy a model without any training

When you need to train a model from scratch on a massive dataset

When you have a small amount of labeled data for a specific task and want to leverage a pre-trained model's knowledge

When you need to reduce the model's latency for real-time inference

Google CloudProfessional Machine Learning Engineer

Domain 3Objective 2

Training Models PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice Questions (Page 1)

Part of the Scaling prototypes into ML models domain, which accounts for ~21% of the PROFESSIONAL-MACHINE-LEARNING-ENGINEER exam.

31questions here

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~21%of the exam

Questions 1–5

1application · medium
A media company wants to train a video classification model. The videos are stored in Cloud Storage, and metadata is in BigQuery. They want to use a custom training job on Vertex AI. What is the recommended way to ingest the video data into the training pipeline?
Select an answer first
2foundation · easy
Which Google Cloud service allows you to run custom ML training jobs on a Kubernetes cluster that you manage?
Select an answer first
3foundation · easy
Which Google Cloud service is primarily designed for storing and analyzing structured tabular data that can be used to train ML models?
Select an answer first
4foundation · easy
A data engineer needs to store a large dataset of labeled images for a computer vision training pipeline. Which Google Cloud service is most appropriate for storing the image files and their labels in a format that integrates well with ML training?
Select an answer first
5foundation · easy
In which scenario is fine-tuning a foundational model most appropriate?
Select an answer first

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Training Models PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice Questions (Page 1)

Questions 1–5

A media company wants to train a video classification model. The videos are stored in Cloud Storage, and metadata is in BigQuery. They want to use a custom training job on Vertex AI. What is the recommended way to ingest the video data into the training pipeline?

Which Google Cloud service allows you to run custom ML training jobs on a Kubernetes cluster that you manage?

Which Google Cloud service is primarily designed for storing and analyzing structured tabular data that can be used to train ML models?

A data engineer needs to store a large dataset of labeled images for a computer vision training pipeline. Which Google Cloud service is most appropriate for storing the image files and their labels in a format that integrates well with ML training?

In which scenario is fine-tuning a foundational model most appropriate?

Keep studying — Scaling prototypes into ML models