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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing Exam |
| Exam Number: | CT-AI |
| Available Languages: | French, Chinese, Korean, Portuguese, German, Japanese, Spanish, English |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) ISTQB Certified Tester Testing with Generative AI (CT-GenAI) |
| Passing Score: | 65% (29/44 points for v2.0; 31/47 points for v1.0) |
| Exam Format: | 1-2 points per question, Multiple-choice questions |
| Exam Price: | €180 - €250 (varies by region and provider) |
| Exam Duration: | 60 (75 for non-native language) |
| Real Exam Qty: | 40 |
| Certificate Validity Period: | Valid indefinitely (no expiration) |
| Recommended Training: | CT-AI Syllabus v2.0 ISTQB Accredited Training Providers |
| Exam Registration: | Pearson VUE ISTQB Official Registration iSQI Exam Registration |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online remote proctored / Onsite test center |
| Pre Condition: | Must hold ISTQB Certified Tester Foundation Level (CTFL) certification |
| Official Syllabus URL: | https://istqb.org/certifications/certified-tester-ai-testing-ct-ai/ |
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NEW QUESTION # 31
A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not. The bank has enough data on past customers to randomly split the data into a training data set and a test/validation data set. A logistic regression model is constructed on the training data set using the following independent variables:
* Gender
* Marital status
* Number of dependents
* Education
* Income
* Loan amount
* Loan term
* Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?
Answer: B
Explanation:
The syllabus mentions that experience-based testing and EDA are effective for detecting biases:
"Experience-based testing can be used to verify that the training dataset is operationally relevant and identify potential sources of bias. EDA is also useful for exploring the data and understanding any relationships that might lead to bias in the model."
NEW QUESTION # 32
A test engineer is planning testing for a wearable medical device using AI. The medical device will detect possible heart issues in patients and dispatch emergency services automatically. It is not expected that many patients will have heart issues, and this is reflected in the available data. In this case, it is decided it is more important that emergency services are not sent un-necessarily, than it is to detect actual heart problems.
Which ONE of the following metrics should the test engineer choose to ensure that the emergency services are sent only when needed?
Answer: D
Explanation:
The correct answer is D. Precision . In this scenario, a positive prediction means that the system detects a possible heart issue and dispatches emergency services. The stated priority is to avoid sending emergency services unnecessarily. That means the test engineer must minimize false positives , because a false positive would incorrectly classify a patient as needing emergency assistance. The CT-AI syllabus states that precision is suitable when the cost of false positives is high and confidence in positive outcomes needs to be high.
Accuracy is a weak choice because the data is imbalanced: most patients are not expected to have heart issues, so a model could appear accurate while still making unacceptable emergency-dispatch errors. MSE and R- squared are regression metrics, not classification metrics, and the problem is a classification decision: dispatch or do not dispatch. Recall would be appropriate if the main concern were not missing true heart issues, but the question explicitly prioritizes avoiding unnecessary dispatches. Therefore, precision is the best functional performance metric for this acceptance objective.
References/topics: CT-AI Syllabus Chapter 5, Sections 5.1 and 5.4 "Selecting ML Functional Performance Metrics."
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NEW QUESTION # 33
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?
Answer: B
Explanation:
Probabilistic and non-deterministic AI-based systemsdo not always produce the same output for identical inputs. This makes traditional testing approaches ineffective. Instead, the best approach is torun tests multiple times and analyze results statistically.
* Statistical Validity:Running tests multiple times ensures that observed results are statistically significant. Instead of relying on a single test run,analyzing multiple iterations helps determine trends, probabilities, and outliers.
* Expected Result Tolerance:AI-based systems may produce different results within an acceptable range. Defining acceptable tolerances (e.g., "result must be within 2% of the optimal value") improves test effectiveness.
* A (Run Several Times for the Same Correct Result):AI systems are ofteninherently non- deterministicand may not return the exact same result every time. Expecting identical outputs contradicts the nature of these systems.
* B & C (Decomposing Tests into Data Ingestion Tests):While data ingestion quality is important, it does notdirectlysolve the issue of probabilistic test results. Statistical analysis is the key approach.
* ISTQB CT-AI Syllabus (Section 8.4: Challenges Testing Probabilistic and Non-Deterministic AI- Based Systems)
* "For probabilistic systems, running a test multiple times may be necessary to obtain a statistically valid test result.".
* "Where a single definitive output is not possible, results should be analyzed statistically rather than relying on individual test cases.".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sinceprobabilistic AI systems do not always return the same result, the best approach is torun multiple test iterations and validate results statistically. Hence, thecorrect answer is D.
NEW QUESTION # 34
Which ONE of the below is MOST likely to indicate a problem with underfitting in an ML model?
Answer: B
Explanation:
The correct answer is D . Underfitting occurs when a model is not sophisticated enough to capture the patterns in the training data. The CT-AI syllabus states that underfitting models tend to be too simplistic and may struggle to provide accurate predictions both for new data and for data very similar to the training data.
Therefore, poor performance on data similar to the training data is the strongest indicator of underfitting.
Option B is more strongly associated with overfitting when considered alone. Overfitting occurs when the model fits the training data too closely and fails to generalize properly to new data. A model that performs well on training-like data but poorly on independent new data is therefore more likely overfitted than underfitted. Option A concerns adversarial robustness, not model fit. Option C concerns performance efficiency or resource consumption, not whether the model has learned the underlying data patterns. For exam purposes, the decisive diagnostic is whether the model is also inaccurate on training-like data; if so, underfitting is the best explanation.
References/topics: CT-AI Syllabus Chapter 3, Sections 3.5.1 "Overfitting" and 3.5.2 "Underfitting."
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NEW QUESTION # 35
Which of the following is an example of overfitting?
Answer: C
Explanation:
Overfitting occurs when a machine learning (ML) model learns patterns that are too specific to the training data, leading to a lack of generalization for new, unseen data. This means the model performs exceptionally well on the training data but poorly on validation or test data because it has memorized the noise and minor details rather than learning the underlying patterns.
NEW QUESTION # 36
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