100% Free ISTQB CT-AI Practice Test Questions

Question 1

Which statement about using AI to analyze reported defects is MOST correct? Choose ONE option (1 out of 4)

A : ML models trained with critical defect tickets can identify defects that cause serious consequences.

B : ML models can support duplicate defect identification when checking defect criticality.

C : ML models can identify categories for a reported defect during assignment.

D : ML models identify developers who should handle a defect based on ticket content.

Answer: C

Question 2

It is possible to use an AI-assisted test automation tool to recognize objects through image processing, rather than using references to their location.

To which type of AI software engineering technology is this example MOST likely referring?

A : Probabilistic Software Engineering

B : Clustering

C : Search-based Software Engineering

D : Classification, Learning and Prediction

Answer: D

Question 3

Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke. The model collects information on each patient regarding their blood pressure, red blood cell count, smoking, status, history of heart disease, cholesterol level, and demographics. Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. One the model is created using a training data set, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model mode a correct or incorrect prediction. The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 2/3 or 0.6667.

A : F1 –source

B : Precision

C : Recall

D : Accuracy

Answer: D

Question 4

Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke. The model collects information on each patient regarding their blood pressure, red blood cell count, smoking, status, history of heart disease, cholesterol level, and demographics. Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. One the model is created using a training data set, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model mode a correct or incorrect prediction. The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 2/3 or 0.6667.

A : F1 –source

B : Precision

C : Recall

D : Accuracy

Answer: D

Question 5

Which statement about using AI to analyze reported defects is MOST correct? Choose ONE option (1 out of 4)

A : ML models trained with critical defect tickets can identify defects that cause serious consequences.

B : ML models can support duplicate defect identification when checking defect criticality.

C : ML models can identify categories for a reported defect during assignment.

D : ML models identify developers who should handle a defect based on ticket content.

Answer: C

100% Free CT-AI Practice Exam Questions and Answers