A retail company is preparing a demand-forecasting model. The final data quality assessment reveals that sales data from the last 3 months is missing for one of the company's five regions. The team must decide whether to proceed to data preparation. What should the team do?

Delay the go decision and investigate why the data is missing and whether it can be recovered.

Proceed but exclude the region with missing data from the model.

Proceed because 3 months of missing data is a small portion of the overall dataset.

Impute the missing sales data using historical averages for that region.

In a go/no-go decision on data readiness, what does a 'no-go' decision indicate?

The data collection process is complete

The data is ready for model training

The model has been deployed to production

The data quality issues are severe enough to halt model development

A data science team is validating a preprocessing pipeline for a fraud-detection model. The pipeline applies log transformation to 'transaction_amount' and label encoding to 'merchant_category'. After running the pipeline, the team checks the output and finds that 'transaction_amount' contains negative values, which should be impossible after a log transformation. What is the most likely cause and the correct next step?

The log transformation was applied to the wrong column; re-run the pipeline with the correct column mapping.

The data contained zero or negative values before transformation; investigate the raw data and the transformation logic.

The label encoding step corrupted the transaction_amount column; re-run only the label encoding step.

The negative values are acceptable because the model can handle them; proceed with training.

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Domain 4Objective 5

Verify Data Quality for Go/no-Go Decision to Conduct Data Preparation PMI-CPMAI Practice Questions (Page 5)

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 25 practice questions to prepare you well beyond it. (estimate)

25questions here

5free pages

6concepts

16%of the exam

Questions 21–25

21application · medium
A retail company is preparing a demand-forecasting model. The final data quality assessment reveals that sales data from the last 3 months is missing for one of the company's five regions. The team must decide whether to proceed to data preparation. What should the team do?
Select an answer first
22foundation · easy
In a go/no-go decision on data readiness, what does a 'no-go' decision indicate?
Select an answer first
23application · medium
A data science team is validating a preprocessing pipeline for a fraud-detection model. The pipeline applies log transformation to 'transaction_amount' and label encoding to 'merchant_category'. After running the pipeline, the team checks the output and finds that 'transaction_amount' contains negative values, which should be impossible after a log transformation. What is the most likely cause and the correct next step?
Select an answer first
24foundation · easy
Which type of bias occurs when the data collection process systematically excludes certain groups or scenarios?
Select an answer first
25foundation · easy
During the final data quality assessment before model training, which dimension focuses on whether the data reflects the correct point in time and is not outdated?
Select an answer first

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Verify Data Quality for Go/no-Go Decision to Conduct Data Preparation PMI-CPMAI Practice Questions (Page 5)

Questions 21–25

A retail company is preparing a demand-forecasting model. The final data quality assessment reveals that sales data from the last 3 months is missing for one of the company's five regions. The team must decide whether to proceed to data preparation. What should the team do?

In a go/no-go decision on data readiness, what does a 'no-go' decision indicate?

Which type of bias occurs when the data collection process systematically excludes certain groups or scenarios?

During the final data quality assessment before model training, which dimension focuses on whether the data reflects the correct point in time and is not outdated?

Keep studying — Manage AI Model Development and Evaluation