A data science team is evaluating a dataset for a model that predicts customer churn. They want to identify whether certain demographic groups are underrepresented in the data. Which data quality assessment technique is most appropriate?

Visualization of the data using a scatter plot

Statistical analysis of class distribution across demographic groups

Data profiling to check data types

A company has deployed a fraud detection model that is retrained monthly. The data engineering team wants to ensure that the quality of incoming data remains consistent over time. Which approach is most effective for ongoing monitoring?

Conduct a one-time data quality audit before the initial deployment

Rely on the model's performance metrics to detect data quality issues

Implement automated data quality checks in the pipeline that alert on anomalies

Manually review a random sample of data each month

A company has a model that predicts equipment failure. The model is retrained weekly, and the data pipeline has been stable for months. Recently, the model's precision has dropped significantly. The data engineering team suspects that the data quality has degraded, but they have no monitoring in place. Which action should they take first to diagnose the issue?

Reduce the frequency of retraining

Retrain the model with more data

Implement data quality monitoring to compare current data distributions with historical baselines

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Domain 4Objective 4

Dataset Quality Issues CT-AI Practice Questions (Page 2)

Part of the Domain 4: ML - Data domain, which makes up ~12% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~3–5 in this domain), expect 1–1 from this objective — we provide 26 practice questions to prepare you well beyond it. (estimate)

26questions here

6free pages

6concepts

Questions 6–10

6foundation · easy
Which data cleaning strategy is most appropriate for removing exact duplicate records from a dataset?
Select an answer first
7application · medium
A data science team is evaluating a dataset for a model that predicts customer churn. They want to identify whether certain demographic groups are underrepresented in the data. Which data quality assessment technique is most appropriate?
Select an answer first
8foundation · easy
Which of the following is a common data quality issue where a data point deviates significantly from the rest of the dataset?
Select an answer first
9application · medium
A company has deployed a fraud detection model that is retrained monthly. The data engineering team wants to ensure that the quality of incoming data remains consistent over time. Which approach is most effective for ongoing monitoring?
Select an answer first
10expert · hard
A company has a model that predicts equipment failure. The model is retrained weekly, and the data pipeline has been stable for months. Recently, the model's precision has dropped significantly. The data engineering team suspects that the data quality has degraded, but they have no monitoring in place. Which action should they take first to diagnose the issue?
Select an answer first

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Dataset Quality Issues CT-AI Practice Questions (Page 2)

Questions 6–10

Which data cleaning strategy is most appropriate for removing exact duplicate records from a dataset?

A data science team is evaluating a dataset for a model that predicts customer churn. They want to identify whether certain demographic groups are underrepresented in the data. Which data quality assessment technique is most appropriate?

Which of the following is a common data quality issue where a data point deviates significantly from the rest of the dataset?

A company has deployed a fraud detection model that is retrained monthly. The data engineering team wants to ensure that the quality of incoming data remains consistent over time. Which approach is most effective for ongoing monitoring?

Keep studying — Domain 4: ML - Data