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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester AI Testing (CT-AI) Exam |
| Exam Number: | CT-AI |
| Exam Duration: | 60 minutes |
| Passing Score: | 65% |
| Available Languages: | English |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| Exam Format: | Multiple Choice |
| Certificate Validity Period: | Lifetime |
| Real Exam Qty: | 40 |
| Recommended Training: | ISTQB Accredited Training Providers |
| Exam Registration: | ISTQB Official Website |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online and onsite proctored exam via accredited ISTQB examination providers |
| Pre Condition: | Recommended prior knowledge of ISTQB Foundation Level (CTFL) and basic understanding of software testing concepts and machine learning fundamentals. |
| Official Syllabus URL: | https://www.istqb.org |
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NEW QUESTION # 15
In which ONE of the following situations would an ML model be MOST effective at determining the criticality of new defects?
Answer: D
Explanation:
The correct answer is C . The CT-AI syllabus states that ML models trained on the features of the most critical defects can be used to identify defects most likely to cause system failures that account for a large proportion of reported defects. This requires meaningful historical defect data and reliable links between defect features and real outcomes.
Option C provides the strongest training basis because defect records are linked to failed tests and production incidents. These links create useful labels and outcome evidence for learning criticality. Option A lacks historical data because the application is new and early in its first test cycle. Option B has many records, but the brand-new team reduces the relevance of people- and assignment-related patterns, and the option does not indicate that records are connected to critical outcomes. Option D is weakened by class imbalance: few critical defects and many non-critical defects make it harder for the model to learn the minority critical class.
The most effective ML setting is therefore an old application with rich, outcome-linked historical data.
References/topics: CT-AI Syllabus Chapter 11, Section 11.2 "Using AI to Analyze Reported Defects."
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NEW QUESTION # 16
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?
Answer: B
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 # 17
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?
Answer: D
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.
Thus Test B cannot be considered a metamorphic follow-up, which makes Option C correct.
NEW QUESTION # 18
Which ONE of the following statements about the hardware used to implement ML systems is MOST likely to be correct?
Answer: A
Explanation:
ML systems often require hardware that supports complex operations, such as matrix multiplications and other linear algebra computations. These operations are fundamental to many machine learning algorithms, particularly in deep learning. Specialist hardware (e.g., GPUs) may be used for efficiency, but complex operations are the core requirement.
NEW QUESTION # 19
Which characteristic of AI-based systems makes it difficult to ensure they are safe (e.g., not harming humans)?
Answer: D
Explanation:
The ISTQB CT-AI syllabus lists several characteristics that make it difficult to ensure safety in AI- based systems. Section2.8 - Safety and AIexplicitly names the characteristics that complicate safety assurance:complexity,non-determinism,probabilistic behavior,self-learning,lack of transparency, andlack of robustness. Among these,complexityis a core challenge because modern AI systems--particularly those using deep learning--have highly non-linear behavior, large numbers of parameters, and intricate interactions that are hard to predict.
Option B (Complexity) directly aligns with the syllabus and is therefore correct.
NEW QUESTION # 20
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