A data scientist is using AutoML to build a regression model. They want to understand which features are most predictive and how AutoML handles feature selection. What should they expect?

AutoML uses a fixed set of features based on the data type.

AutoML only uses all features and does not perform any feature selection.

AutoML requires the user to manually specify the features to include.

AutoML automatically evaluates feature importance and may drop irrelevant features during model training.

A startup needs to quickly develop a predictive model for customer lifetime value. The team has limited ML expertise and wants to compare several algorithms without writing extensive code. What is the most efficient approach?

Use Databricks AutoML to automatically train and compare multiple algorithms with hyperparameter tuning.

Manually implement logistic regression, decision trees, and gradient boosting, then compare their performance.

Use a single deep learning model with default hyperparameters.

Hire a consultant to build and compare models externally.

A data scientist is using AutoML to build a model for a dataset with a mix of numerical and categorical features. They want to ensure that the feature selection process is appropriate for this data type. What should they expect AutoML to do?

AutoML will only use numerical features and ignore categorical ones.

AutoML will create separate models for numerical and categorical features.

AutoML requires the user to manually encode categorical features before training.

AutoML will automatically handle encoding and feature selection for both numerical and categorical features.

In Databricks AutoML, what is the primary role of feature selection during the automated training process?

To remove all features with missing values

To generate new synthetic features from existing ones

To convert categorical features into numerical ones

To identify and retain the most relevant features for the model

How does AutoML improve accessibility for non-experts in machine learning?

By providing a graphical interface only for visualization

By limiting the number of algorithms to one

By automating complex steps like algorithm selection and hyperparameter tuning

By requiring users to write custom code for each step

DatabricksCertified Machine Learning Associate

Domain 1Objective 3

AutoML and ML Runtime MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 2)

Part of the Section 1: Databricks Machine Learning domain, which makes up ~19% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~7–11 in this domain), expect 2–3 from this objective — we provide 21 practice questions to prepare you well beyond it. (estimate)

21questions here

5free pages

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Questions 6–10

6application · medium
A data scientist is using AutoML to build a regression model. They want to understand which features are most predictive and how AutoML handles feature selection. What should they expect?
Select an answer first
7application · medium
A startup needs to quickly develop a predictive model for customer lifetime value. The team has limited ML expertise and wants to compare several algorithms without writing extensive code. What is the most efficient approach?
Select an answer first
8expert · hard
A data scientist is using AutoML to build a model for a dataset with a mix of numerical and categorical features. They want to ensure that the feature selection process is appropriate for this data type. What should they expect AutoML to do?
Select an answer first
9foundation · easy
In Databricks AutoML, what is the primary role of feature selection during the automated training process?
Select an answer first
10foundation · easy
How does AutoML improve accessibility for non-experts in machine learning?
Select an answer first

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