A QA team wants to identify recurring patterns in defect reports, such as defects that occur only on a specific browser or after a particular user action. They have thousands of unstructured reports. Which technique should they use to automatically discover these patterns?

Apply clustering to group defects by similarity in text and metadata, then analyze each cluster for common attributes.

Use a supervised classifier to label each defect as 'browser-related' or 'not browser-related'.

Use a predictive model to forecast which defects will be reopened.

Use a simple keyword search for 'browser' and 'click' to find related defects.

A QA team receives hundreds of defect reports weekly. Many reports use different wording for the same underlying issue (e.g., 'app crashes on login' vs 'login screen freezes'). The team wants to automatically group these related reports to reduce duplicate analysis effort. Which approach should they implement?

Use an NLP-based clustering model that vectorizes report text and groups similar reports by semantic similarity.

Train a predictive model on historical defect data to forecast which components will have defects next release.

Manually review a sample of reports and create a static list of common defect titles for the team to reference.

Apply a rule-based filter that matches exact keywords like 'crash' and 'freeze' to categorize reports.

A product team receives a large number of defect reports each day. They want to use AI to prioritize which defects should be fixed first, considering the severity of the impact on users and the likelihood of occurrence. Which AI approach best fits this need?

Train a model that assigns a priority score based on historical data of defect impact and frequency.

Use NLP to extract the defect description and manually assign priority based on keywords.

Cluster defects by component and prioritize the component with the most defects.

Use a predictive model to forecast the total number of defects in the next release.

What is the primary benefit of using AI to automate the analysis of reported defects?

It eliminates the need for human testers to review any defect reports.

It speeds up the identification of root causes and patterns across many defect reports.

It replaces the need for version control and test management tools.

It guarantees that all defects are fixed automatically without human intervention.

Certified Tester AI Testing

Domain 11Objective 2

Using AI to Analyze Reported Defects CT-AI Practice Questions (Page 3)

Part of the Domain 11: Using AI for Testing domain, which makes up ~11% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~3–4 in this domain), expect 1–1 from this objective — we provide 23 practice questions to prepare you well beyond it. (estimate)

23questions here

5free pages

6concepts

Questions 11–15

11application · medium
A QA team wants to identify recurring patterns in defect reports, such as defects that occur only on a specific browser or after a particular user action. They have thousands of unstructured reports. Which technique should they use to automatically discover these patterns?
Select an answer first
12application · medium
A QA team receives hundreds of defect reports weekly. Many reports use different wording for the same underlying issue (e.g., 'app crashes on login' vs 'login screen freezes'). The team wants to automatically group these related reports to reduce duplicate analysis effort. Which approach should they implement?
Select an answer first
13application · medium
A product team receives a large number of defect reports each day. They want to use AI to prioritize which defects should be fixed first, considering the severity of the impact on users and the likelihood of occurrence. Which AI approach best fits this need?
Select an answer first
14foundation · easy
What is the primary benefit of using AI to automate the analysis of reported defects?
Select an answer first
15foundation · easy
Which NLP technique is commonly used to extract key information like error codes or component names from unstructured defect reports?
Select an answer first

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Using AI to Analyze Reported Defects CT-AI Practice Questions (Page 3)

Questions 11–15

A QA team wants to identify recurring patterns in defect reports, such as defects that occur only on a specific browser or after a particular user action. They have thousands of unstructured reports. Which technique should they use to automatically discover these patterns?

A product team receives a large number of defect reports each day. They want to use AI to prioritize which defects should be fixed first, considering the severity of the impact on users and the likelihood of occurrence. Which AI approach best fits this need?

What is the primary benefit of using AI to automate the analysis of reported defects?

Which NLP technique is commonly used to extract key information like error codes or component names from unstructured defect reports?

Keep studying — Domain 11: Using AI for Testing