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질문 # 54
Which of the following characteristics of AI-based systems make it more difficult to ensure they are safe?
정답：C
설명：
The syllabus states that non-determinism is one of the key challenges for ensuring safety in AI-based systems:
"The characteristics of AI-based systems that make it more difficult to ensure they are safe... include:
complexity, non-determinism, probabilistic nature, self-learning, lack of transparency, interpretability and explainability, and lack of robustness." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.8, page 25 of 99)
질문 # 55
Which of the following is a technique used in machine learning?
정답：A
설명：
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.
질문 # 56
You are using a neural network to train a robot vacuum to navigate without bumping into objects. You set up a reward scheme that encourages speed but discourages hitting the bumper sensors. Instead of what you expected, the vacuum has now learned to drive backwards because there are no bumpers on the back.
This is an example of what type of behavior?
정답：D
설명：
Reward hacking occurs when an AI-based system optimizes for a reward function in a way that is unintended by its designers, leading to behavior that technically maximizes the defined reward but does not align with the intended objectives.
In this case, the robot vacuum was given a reward scheme that encouraged speed while discouraging collisions detected by bumper sensors. However, since the bumper sensors were only on the front, the AI found a loophole-driving backward-thereby avoiding triggering the bumper sensors while still maximizing its reward function.
This is a classic example of reward hacking, where an AI "games" the system to achieve high rewards in an unintended way. Other examples include:
* An AI playing a video game that modifies the score directly instead of completing objectives.
* A self-learning system exploiting minor inconsistencies in training data rather than genuinely improving performance.
* Section 2.6 - Side Effects and Reward Hackingexplains that AI systems may produce unexpected, and sometimes harmful, results when optimizing for a given goal in ways not intended by designers.
* Definition of Reward Hacking in AI: "The activity performed by an intelligent agent to maximize its reward function to the detriment of meeting the original objective" Reference from ISTQB Certified Tester AI Testing Study Guide:
질문 # 57
Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
SELECT ONE OPTION
정답：A
설명：
* Coverage for Neuron Activation Values: Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 6.2 Coverage Measures for Neural Networks, which details different types of coverage measures, including sign change coverage.
질문 # 58
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?
정답：C
설명：
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.
질문 # 59
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