A data science team is working on a fraud detection model. They have a dataset with transaction timestamps, amounts, and merchant categories. They believe that the time since the last transaction and the average transaction amount per merchant could improve model performance. Which process should they follow to create these new features?

Data normalization: scale the existing features

Feature selection: choose the most relevant existing features

Data augmentation: generate synthetic samples

Feature engineering: create new features by aggregating and transforming raw data

Which practice helps ensure reproducibility in data transformation?

Scaling features to a range of 0 to 1

Selecting features using a filter method

What is the primary goal of a data cleaning workflow?

To create new synthetic samples for training

To improve data quality by addressing issues like missing values and outliers

To reduce the number of features in the dataset

To transform data to have zero mean and unit variance

A data science team is building a predictive model for a marketing campaign with 200 features. They have a limited dataset of 5,000 records. They need to reduce dimensionality to avoid overfitting, but they also need to maintain model interpretability for business stakeholders. They are considering using Lasso regression (embedded method) or recursive feature elimination (wrapper method). Which approach is more appropriate given the constraints?

Use principal component analysis to reduce dimensionality

Use a filter method like chi-squared test to select top features

Use recursive feature elimination with a support vector machine

Use Lasso regression to select features during model training

Which technique is an example of feature engineering?

Selecting the top 10 features using a filter method

Creating a new feature by combining two existing columns

Removing duplicate rows from the dataset

Applying min-max scaling to a feature

PMICertified Professional in Managing AI (PMI-CPMAI)

Domain 4Objective 4

Manage Data Transformation to Conduct Data Preparation PMI-CPMAI Practice Questions (Page 1)

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 1–5

1application · medium
A data science team is working on a fraud detection model. They have a dataset with transaction timestamps, amounts, and merchant categories. They believe that the time since the last transaction and the average transaction amount per merchant could improve model performance. Which process should they follow to create these new features?
Select an answer first
2foundation · easy
Which practice helps ensure reproducibility in data transformation?
Select an answer first
3foundation · easy
What is the primary goal of a data cleaning workflow?
Select an answer first
4expert · hard
A data science team is building a predictive model for a marketing campaign with 200 features. They have a limited dataset of 5,000 records. They need to reduce dimensionality to avoid overfitting, but they also need to maintain model interpretability for business stakeholders. They are considering using Lasso regression (embedded method) or recursive feature elimination (wrapper method). Which approach is more appropriate given the constraints?
Select an answer first
5foundation · easy
Which technique is an example of feature engineering?
Select an answer first

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Manage Data Transformation to Conduct Data Preparation PMI-CPMAI Practice Questions (Page 1)

Questions 1–5

Which practice helps ensure reproducibility in data transformation?

What is the primary goal of a data cleaning workflow?

Which technique is an example of feature engineering?

Keep studying — Manage AI Model Development and Evaluation