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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester AI Testing (CT-AI) Exam |
| Exam Number: | CT-AI |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| Exam Duration: | 60 minutes |
| Available Languages: | English |
| Certificate Validity Period: | Lifetime |
| Passing Score: | 65% |
| Real Exam Qty: | 40 |
| Exam Format: | Multiple Choice |
| 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 # 51
Which ONE of the following statements is MOST true about black box adversarial testing?
Answer: C
Explanation:
The correct answer is B . The CT-AI syllabus explains that black-box adversarial attacks involve exploring the model to determine its functionality and then building a duplicate model with similar functionality. The attacker then uses a white-box approach on the duplicate model to identify adversarial examples. Because adversarial examples are generally transferable, those examples will normally also work on the original model.
Option A describes white-box adversarial testing, where the algorithm, model settings, and parameters are known. Option C describes data poisoning , where training data is manipulated to induce incorrect future predictions or create vulnerabilities. Option D describes pairwise or broader combinatorial testing, which is used where many parameters and values make exhaustive testing impractical. Black-box adversarial testing is instead based on limited knowledge of the target model and the empirical transferability of adversarial examples. If a duplicate model cannot be built, the syllabus also notes that high-volume automated testing may be used to discover adversarial examples and observe results.
References/topics: CT-AI Syllabus Chapter 9, Section 9.1.1 "Adversarial Attacks"; Section 9.1.2 "Data Poisoning."
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NEW QUESTION # 52
Which of the following statements about the structure and function of neural networks is true?
Answer: A
Explanation:
Section1.7 - Neural Networksof the ISTQB CT-AI syllabus explains that neural networks consist of neurons connected by weighted links. During training,learning occurs by adjusting the weights on these connections. This is the essence of gradient descent and backpropagation. Option B correctly states this behavior: only the weights are modified, not the activation functions, neuron counts, or architectural structure.
NEW QUESTION # 53
Which of the following is a technique used in machine learning?
Answer: B
Explanation:
Decision trees are a widely usedmachine learning (ML) techniquethat falls undersupervised learning. They are used for bothclassification and regressiontasks and are popular due to their interpretability and effectiveness.
* How Decision Trees Work:
* The model splits the dataset into branches based on feature conditions.
* It continues to divide the data until each subset belongs to a single category (classification) or predicts a continuous value (regression).
* The final result is a tree structure where decisions are made atnodes, and predictions are given at leaf nodes.
* Common Applications of Decision Trees:
* Fraud detection
* Medical diagnosis
* Customer segmentation
* Recommendation systems
* B (Equivalence Partitioning):This is asoftware testing technique, not a machine learning method. It is used to divide input data into partitions to reduce test cases while maintaining coverage.
* C (Boundary Value Analysis):Anothersoftware testing technique, used to check edge cases around input boundaries.
* D (Decision Tables):A structuredtesting techniqueused to validate business rules and logic, not a machine learning method.
* ISTQB CT-AI Syllabus (Section 3.1: Forms of Machine Learning - Decision Trees)
* "Decision trees are used in classification and regression models and are fundamental ML algorithms".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sincedecision trees are a core technique in machine learning, while the other options are software testing techniques, thecorrect answer is A.
NEW QUESTION # 54
Which ONE of the below is NOT likely to cause a data quality issue affecting a single ML model?
Answer: C
Explanation:
The correct answer is C. Incorrect weights . The CT-AI syllabus lists typical dataset quality issues, including wrong data, incomplete data, mislabeled data, insufficient data, data not pre-processed, obsolete data, unbalanced data, unfair data, duplicate data, irrelevant data, privacy issues, and security issues. It specifically notes that wrong data may arise from a faulty sensor, and that incomplete data may be caused by security issues, hardware issues, or human error. Security issues may also involve fraudulent or misleading data deliberately inserted into training data.
Incorrect weights are different in nature. Weights are parameters learned within certain ML models, especially neural networks; they are part of the trained model, not a characteristic of the dataset itself. Incorrect or poorly optimized weights may produce poor predictions, but they do not constitute a data quality issue in the dataset. By contrast, faulty sensors can create wrong captured data, hardware issues can lead to missing data, and security issues can compromise or poison data.
References/topics: CT-AI Syllabus Chapter 4, Section 4.3 "Dataset Quality Issues"; Section 4.4 "Data Quality and its Effect on the ML Model."
NEW QUESTION # 55
Which of the below TWO examples of AI system behaviour are reward hacking?
i. An AI medical device intended to keep a patient stable may give the patient a treatment that means they recover less quickly.
ii. An AI system intended to maximise production of a commodity by weight allows quality and size of product to reduce.
iii. An AI system intended to ensure the output of a factory process is always sorted correctly, destroys the outputs.
iv. An AI system intended to remove security vulnerabilities from software code, removes all functionality that has security vulnerabilities.
Answer: D
Explanation:
The correct answer is C , covering examples iii and iv . The CT-AI syllabus defines reward hacking as the situation where an AI-based system achieves a specified goal through a "clever" or "easy" solution that perverts the designer's intent. The goal is technically satisfied, but in a way that defeats the real purpose of the system. The syllabus gives the example of an AI system targeting a highest score by hacking the stored score rather than playing the game properly.
Example iii is reward hacking because destroying the outputs may make the remaining sorting condition trivially true, but it violates the production objective. Example iv is also reward hacking because removing all vulnerable functionality eliminates vulnerabilities by destroying useful system behaviour. Examples i and ii are better categorized as negative side effects : the system pursues a stated goal while ignoring broader quality or stakeholder consequences, such as recovery speed or product quality.
References/topics: CT-AI Syllabus Chapter 2, Section 2.6 "Side Effects and Reward Hacking."
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NEW QUESTION # 56
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