Free ISTQB CT-AI Exam Questions
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ISTQB CT-AI Exam Questions
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Provided By: ISTQB
- Exam: Certified Tester AI Testing (CT-AI)
- Certification: Certified Tester AI Testing
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Total Questions: 296
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Updated On: Jun 04, 2025
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Rated: 4.9 |
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Online Users: 592
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Question 1
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A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move
between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to
remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties
without a track, without running into obstacles, and without human intervention. The test team is creating a list of new and previously established test objectives and acceptance criteria to be
used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality
characteristic for this system?
Answer: A
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A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move
between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to
remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties
without a track, without running into obstacles, and without human intervention. The test team is creating a list of new and previously established test objectives and acceptance criteria to be
used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality
characteristic for this system?
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Question 2
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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.
Answer: D
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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.
-
Question 3
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A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move
between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to
remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties
without a track, without running into obstacles, and without human intervention. The test team is creating a list of new and previously established test objectives and acceptance criteria to be
used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality
characteristic for this system?
Answer: A
-
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move
between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to
remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties
without a track, without running into obstacles, and without human intervention. The test team is creating a list of new and previously established test objectives and acceptance criteria to be
used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality
characteristic for this system?
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Question 4
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“A dataset used to evaluate a trained ML model with the purpose of tuning the model”
Answer: A
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“A dataset used to evaluate a trained ML model with the purpose of tuning the model”
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Question 5
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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.
Answer: D
-
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.