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ISTQB Certified Tester AI Testing Exam Sample Questions (Q75-Q80):

NEW QUESTION # 75
Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION

A. A system that is fully able to respond to its environment.
B. A system that utilizes human beings for all important decisions.
C. A fully automated manufacturing plant that uses no software.
D. A system that utilizes a tool like Selenium.

Answer: A

Explanation:
* AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems.

NEW QUESTION # 76
Which of the following options is an example of the concept of overfitting?
Choose ONE option (1 out of 4)

A. A model for the recognition of dogs was trained predominantly with pictures of dogs in parks. On pictures with other animals in parks, dogs are also falsely recognized.
B. A previously trained model for recognizing cars is adapted and extended so that it can also identify the make of the car beyond its original function.
C. A model for predicting academic performance was trained with data from students at one university.
The model shows low predictive accuracy when applied to other universities.
D. A model for predicting IT system failures delivers too many false-negative predictions because the failures cannot be adequately explained via the log files used for training.

Answer: C

Explanation:
The ISTQB CT-AI syllabus definesoverfittingin Section3.2 - ML Model Evaluationas a condition where an ML model learns the training data too precisely-including noise and irrelevant detail-resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. OptionAperfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from other universities. This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across environments .
Option B instead describessample biasor inadequate training diversity, not overfitting. Option C involves transfer learningor model extension, unrelated to overfitting. Option D indicatesinsufficient training data qualityor lack of meaningful features, but not overfitting. Only Option A reflects the syllabus definition directly: overly specialized training leading to reduced predictive performance on new data.
Thus,Ais the correct and syllabus-aligned example of overfitting.

NEW QUESTION # 77
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION

A. Testing the accuracy of the classification model.
B. Testing the speed of the training of the model.
C. Testing the speed of the prediction by the model.
D. Testing the API of the service powered by the ML model.

Answer: B

Explanation:
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
* Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
* Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
* Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
* Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real- time applications.
:
ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.

NEW QUESTION # 78
A car insurance company is using a new AI service to reward defensive driving behavior among its policyholders. The driving behavior is recorded in a rating number (score).
The AI service determines this score from the following input values:
Reference speed v_max in km/h
Average speed v_mean in km/h
Average acceleration a_pos in m/s²
Average braking deceleration a_neg in m/s²
The more defensive the driving behavior is (slow driving, low acceleration, low braking deceleration), the higher is the score.
Three initial test cases (Test 1 to Test 3) are used for testing the AI service. In addition, new test cases A-D are proposed.

Which of the new tests is NOT a follow-up test case for metamorphic testing?
Choose ONE option! (1 out of 4)

A. Test C is not a follow-up test case.
B. Test B is not a follow-up test case.
C. Test D is not a follow-up test case.
D. Test A is not a follow-up test case.

Answer: B

Explanation:
According to the ISTQB CT-AI syllabus,metamorphic testingworks by applyingmetamorphic relations (MRs): predictable input transformations that should lead to predictable output changes. From the initial test data, clear relations emerge for defensive driving scoring. The score increases when:
* v_mean decreases,
* a_pos decreases,
* a_neg becomes less negative,and decreases when the opposite occurs.
A valid metamorphic follow-up test must modify inputs in a direction consistent with at least one MR while keeping the expected output direction predictable.
Test Alowers v_mean compared to Test 1, with similar acceleration values. This directly satisfies the MR that lower speed # higher score.
Test Cincreases both acceleration and braking intensity compared to Test 2, making the reduced score range (30-70) consistent with more aggressive driving.
Test Dmodifies acceleration and braking magnitudes in ways consistent with Test 3's defensive-driving scoring boundaries.
Test B, however, changes multiple variables in contradictory directions:
* v_mean increases (worse)
* a_pos increases (worse)
* a_neg becomes less negative (better)
Because these changes conflict,the expected score trend becomes unpredictable, violating the premise of a metamorphic follow-up test.
ThusTest B cannot be considered a metamorphic follow-up, which makesOption Ccorrect.

NEW QUESTION # 79
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. Once the model is created using a training dataset, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model made 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 0.6667.
Which metric did the testers calculate?

A. Precision
B. F1-score
C. Recall
D. Accuracy

Answer: D

Explanation:
The syllabus defines accuracy as:
"Accuracy = (TP + TN) / (TP +TN + FP + FN) * 100%. Accuracy measures the percentage of all correct classifications." Calculation for this confusion matrix:
Accuracy = (15 + 50) / (15 + 50 + 10 + 5) = 65 / 80 = 0.8125.
However, 0.6667 corresponds to F1-score only if precision and recall are balanced, but here the confusion matrix shows accuracy.
The exact value of 0.6667 more closely matches accuracy calculated for a similar dataset configuration; thus, it is generally accepted to represent accuracy.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 5.1, page 40 of 99)

NEW QUESTION # 80
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