Before training a model, a team splits a dataset into training, validation, and test sets. To ensure that the model evaluation is unbiased, which practice should be applied when creating these splits?

Splitting the data in the order it was collected

Using only the training set for validation

Assigning all rare class samples to the test set

Randomly shuffling the data before splitting

A text classification dataset has 1,000 spam messages and 100 legitimate messages. The team wants to generate new synthetic legitimate messages to balance the dataset. Which approach is a form of synthetic data generation for text?

Using a generative language model to create new legitimate message examples

Randomly deleting 900 spam messages

Reordering the words in each spam message

Duplicating each legitimate message 10 times

AWSCertified Machine Learning Engineer - Associate

Domain 1Objective 3

Task 1.3: Ensure Data Integrity and Prepare Data for Modeling MLA-C01 Practice Questions (Page 3)

Part of the Content Domain 1: Data Preparation for Machine Learning (ML) domain, which accounts for 28% of the MLA-C01 exam. AWS does not publish an official question count, but from its 130-minute exam (~50–85 total, ~14–24 in this domain), expect 5–8 from this objective — we provide 28 practice questions to prepare you well beyond it. (estimate)

28questions here

6free pages

9concepts

28%of the exam

Questions 11–15

11foundation · easy
A team needs to provide a shared, POSIX-compliant file system that can be mounted on multiple Amazon EC2 instances for distributed model training. Which AWS storage service is designed for this use case?
Select an answer first
12foundation · easy
Before training a model, a team splits a dataset into training, validation, and test sets. To ensure that the model evaluation is unbiased, which practice should be applied when creating these splits?
Select an answer first
13foundation · easy
A dataset contains income values that were self-reported by users. The team suspects that some users inaccurately reported their income, leading to systematic errors in the data. Which type of bias is this?
Select an answer first
14foundation · easy
A dataset contains a column with full names of individuals. The team wants to protect this data by replacing each name with a generic placeholder like 'USER_001'. Which data protection technique is being applied?
Select an answer first
15foundation · easy
A text classification dataset has 1,000 spam messages and 100 legitimate messages. The team wants to generate new synthetic legitimate messages to balance the dataset. Which approach is a form of synthetic data generation for text?
Select an answer first

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Task 1.3: Ensure Data Integrity and Prepare Data for Modeling MLA-C01 Practice Questions (Page 3)

Questions 11–15

A team needs to provide a shared, POSIX-compliant file system that can be mounted on multiple Amazon EC2 instances for distributed model training. Which AWS storage service is designed for this use case?

Before training a model, a team splits a dataset into training, validation, and test sets. To ensure that the model evaluation is unbiased, which practice should be applied when creating these splits?

A dataset contains income values that were self-reported by users. The team suspects that some users inaccurately reported their income, leading to systematic errors in the data. Which type of bias is this?

A dataset contains a column with full names of individuals. The team wants to protect this data by replacing each name with a generic placeholder like 'USER_001'. Which data protection technique is being applied?

A text classification dataset has 1,000 spam messages and 100 legitimate messages. The team wants to generate new synthetic legitimate messages to balance the dataset. Which approach is a form of synthetic data generation for text?

Keep studying — Content Domain 1: Data Preparation for Machine Learning (ML)