In a BigQuery ML classification model, what is the role of the label column in the training data?

It determines the evaluation metrics automatically.

It defines the ground truth the model learns to predict.

It provides the input features for the model.

It specifies the model type to be used.

A company wants to use Vertex AI AutoML to train a custom image classification model to identify defective products on a manufacturing line. They have a dataset of product images stored in Cloud Storage, with labels indicating 'defective' or 'non-defective'. They want to use the Agent Platform (Vertex AI) AutoML to train the model. What is the first step they should take?

Launch a training job directly by specifying the Cloud Storage path

Create a BigQuery table to store the image metadata and labels

Create a Vertex AI dataset and import the labeled images from Cloud Storage

Deploy a pre-trained model from the Model Garden

What is the first step in training a custom model using Agent Platform AutoML?

Configuring the training options

Google CloudProfessional Machine Learning Engineer

Domain 1Objective 1

Developing ML Models Using BigQuery ML or AutoML on Gemini Enterprise Agent Platform PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice Questions (Page 3)

Part of the Architecting low-code AI solutions domain, which accounts for ~13% of the PROFESSIONAL-MACHINE-LEARNING-ENGINEER exam.

26questions here

6free pages

10concepts

~13%of the exam

10 Concepts in This Objective

Selecting BigQuery ML model types
Building classification models in BigQuery ML
Building regression models in BigQuery ML
Building forecasting models in BigQuery ML
Building clustering models in BigQuery ML
Performing feature engineering in BigQuery ML
Performing feature selection in BigQuery ML
Generating predictions with BigQuery ML
Training models with Agent Platform AutoML
Fine-tuning Gemini models using BigQuery

Questions 11–15

11foundation · easy
A data scientist wants to identify which features are most influential in a trained BigQuery ML classification model. Which BigQuery ML function can be used to retrieve feature importance scores?
Select an answer first
12foundation · easy
Which BigQuery SQL function can be used to transform a categorical string column into a numeric representation for a BigQuery ML model?
Select an answer first
13foundation · easy
In a BigQuery ML classification model, what is the role of the label column in the training data?
Select an answer first
14application · medium
A company wants to use Vertex AI AutoML to train a custom image classification model to identify defective products on a manufacturing line. They have a dataset of product images stored in Cloud Storage, with labels indicating 'defective' or 'non-defective'. They want to use the Agent Platform (Vertex AI) AutoML to train the model. What is the first step they should take?
Select an answer first
15foundation · easy
What is the first step in training a custom model using Agent Platform AutoML?
Select an answer first

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Developing ML Models Using BigQuery ML or AutoML on Gemini Enterprise Agent Platform PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice Questions (Page 3)

Questions 11–15

A data scientist wants to identify which features are most influential in a trained BigQuery ML classification model. Which BigQuery ML function can be used to retrieve feature importance scores?

Which BigQuery SQL function can be used to transform a categorical string column into a numeric representation for a BigQuery ML model?

In a BigQuery ML classification model, what is the role of the label column in the training data?

What is the first step in training a custom model using Agent Platform AutoML?

Keep studying — Architecting low-code AI solutions