What is the purpose of synthetic data generation?

To scale features to a common range

To select the most relevant features

To remove outliers from the dataset

To create artificial data that mimics real data distributions

What is the purpose of data normalization?

To bring features to a comparable range without distorting differences

To transform data to have zero mean and unit variance

To remove duplicate records from the dataset

To create new features from existing ones

A data science team is building a model to predict customer lifetime value. The dataset contains missing values in several features, including 'income' and 'purchase_frequency'. They also notice that some customers have duplicate records. The team is considering whether to impute missing values before or after removing duplicates. Which approach is more appropriate?

Impute missing values first, then remove duplicates

Remove duplicates first, then impute missing values

Ignore both issues and proceed with model training

Remove duplicates and impute missing values simultaneously in a single step

A data science team is working on a regulatory project that requires an audit trail of all data transformations applied to the dataset. They need to document the rationale, steps, and outputs of each transformation so that auditors can understand the process. Which practice should they implement?

Maintain a transformation log that records each step, its purpose, and the resulting output

Document only the final transformed dataset

Verbally explain the transformations to the auditors

Keep the transformation code in a shared repository without comments

What is the purpose of documenting data transformation processes?

To reduce the number of features

To increase the speed of data processing

To support auditability and collaboration

PMICertified Professional in Managing AI (PMI-CPMAI)

Domain 4Objective 4

Manage Data Transformation to Conduct Data Preparation PMI-CPMAI Practice Questions (Page 3)

Part of the Manage AI Model Development and Evaluation domain, which accounts for 16% of the PMI-CPMAI exam. PMI does not publish an official question count, but from its 160-minute exam (~65–105 total, ~10–17 in this domain), expect 2–3 from this objective — we provide 36 practice questions to prepare you well beyond it. (estimate)

36questions here

8free pages

10concepts

16%of the exam

10 Concepts in This Objective

Data Cleaning Workflows
Data Preprocessing Pipelines
Feature Engineering Techniques
Feature Selection Methods
Data Normalization Methods
Data Standardization Processes
Data Augmentation Strategies
Synthetic Data Generation
Reproducibility in Data Transformation
Documentation of Transformation Processes

Questions 11–15

11foundation · easy
What is the purpose of synthetic data generation?
Select an answer first
12foundation · easy
What is the purpose of data normalization?
Select an answer first
13expert · hard
A data science team is building a model to predict customer lifetime value. The dataset contains missing values in several features, including 'income' and 'purchase_frequency'. They also notice that some customers have duplicate records. The team is considering whether to impute missing values before or after removing duplicates. Which approach is more appropriate?
Select an answer first
14application · medium
A data science team is working on a regulatory project that requires an audit trail of all data transformations applied to the dataset. They need to document the rationale, steps, and outputs of each transformation so that auditors can understand the process. Which practice should they implement?
Select an answer first
15foundation · easy
What is the purpose of documenting data transformation processes?
Select an answer first

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