Which metric is most appropriate for evaluating a regression model's performance when the scale of the target variable varies and you want a relative measure of error?

Mean Absolute Percentage Error (MAPE)

What is the primary purpose of using cross-validation in model evaluation?

To ensure the model is trained on all available data at once

To increase the size of the training dataset

To eliminate the need for a separate test set

To reduce the variance of the performance estimate compared to a single train-test split

A data scientist is building a model on a time-series dataset with strong seasonal patterns. The goal is to compare two forecasting models. Which cross-validation approach is most appropriate?

Time-series cross-validation (expanding window)

Stratified k-fold cross-validation

Certified Artificial Intelligence Practitioner (CAIP)

Domain 3Objective 4

Objective 3.4 Evaluate the Model AIP-210 Practice Questions (Page 7)

Part of the 3.0 Training and Tuning ML Systems and Models domain, which accounts for 24% of the AIP-210 exam.

37questions here

8free pages

10concepts

24%of the exam

Questions 31–35

31foundation · easy
Which metric is most appropriate for evaluating a regression model's performance when the scale of the target variable varies and you want a relative measure of error?
Select an answer first
32foundation · easy
When comparing multiple classification models, which metric is most appropriate when the dataset is highly imbalanced (e.g., 95% negative, 5% positive)?
Select an answer first
33foundation · easy
What is the primary purpose of using cross-validation in model evaluation?
Select an answer first
34expert · hard
A data scientist is building a model on a time-series dataset with strong seasonal patterns. The goal is to compare two forecasting models. Which cross-validation approach is most appropriate?
Select an answer first
35expert · hard
A credit risk model has a recall of 0.9 for defaulters and a precision of 0.3. The bank approves loans to all non-defaulters, but each default costs $5,000, and each approved loan to a non-defaulter generates $100 profit. The model is used to reject applicants predicted to default. What is the net financial impact per rejected applicant?
Select an answer first

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Objective 3.4 Evaluate the Model AIP-210 Practice Questions (Page 7)

Questions 31–35

Which metric is most appropriate for evaluating a regression model's performance when the scale of the target variable varies and you want a relative measure of error?

When comparing multiple classification models, which metric is most appropriate when the dataset is highly imbalanced (e.g., 95% negative, 5% positive)?

What is the primary purpose of using cross-validation in model evaluation?

A data scientist is building a model on a time-series dataset with strong seasonal patterns. The goal is to compare two forecasting models. Which cross-validation approach is most appropriate?

Keep studying — 3.0 Training and Tuning ML Systems and Models