Which method is used for synthetic data generation?

Generative adversarial networks (GANs)

A data science team is building a model to predict loan default. They have features like 'credit_score', 'annual_income', and 'loan_amount'. They suspect that the ratio of loan amount to annual income might be a better predictor than the raw features. They also want to reduce the number of features to avoid overfitting. Which approach should they take?

Apply PCA to create new features

Use all raw features without creating new ones

Use a wrapper method to select features before creating new ones

Create a new feature 'loan_to_income_ratio' and then use feature selection to remove less relevant features

A data science team is training a model to recognize handwritten digits. They have a small dataset of 5,000 images per class. They want to improve model generalization by augmenting the data with random rotations and flips. However, they are concerned that aggressive augmentation might distort the digits and hurt performance. Which approach should they take?

Do not use augmentation and rely on the original dataset

Apply aggressive augmentation (e.g., large rotations and flips) to maximize data diversity

Apply mild augmentation (e.g., small rotations and flips) and validate on a holdout set

Use augmentation only on the test set

Which formula represents z-score standardization?

(x - mean) / standard deviation

(x - median) / interquartile range

PMICertified Professional in Managing AI (PMI-CPMAI)

Domain 4Objective 4

Manage Data Transformation to Conduct Data Preparation PMI-CPMAI Practice Questions (Page 4)

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 16–20

16foundation · easy
Which method is used for synthetic data generation?
Select an answer first
17expert · hard
A data science team is building a model to predict loan default. They have features like 'credit_score', 'annual_income', and 'loan_amount'. They suspect that the ratio of loan amount to annual income might be a better predictor than the raw features. They also want to reduce the number of features to avoid overfitting. Which approach should they take?
Select an answer first
18foundation · easy
Which technique is commonly used for data augmentation?
Select an answer first
19expert · hard
A data science team is training a model to recognize handwritten digits. They have a small dataset of 5,000 images per class. They want to improve model generalization by augmenting the data with random rotations and flips. However, they are concerned that aggressive augmentation might distort the digits and hurt performance. Which approach should they take?
Select an answer first
20foundation · easy
Which formula represents z-score standardization?
Select an answer first

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Manage Data Transformation to Conduct Data Preparation PMI-CPMAI Practice Questions (Page 4)

Questions 16–20

Which method is used for synthetic data generation?

Which technique is commonly used for data augmentation?

Which formula represents z-score standardization?

Keep studying — Manage AI Model Development and Evaluation