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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Input Data Testing for Machine Learning Systems | 20% | - Validation of data pipelines and preprocessing - Data quality attributes - Label verification and ground truth assessment - Testing for bias, representativeness, and completeness |
| Topic 2: Introduction to Testing AI-Based Systems | 10% | - Challenges in testing AI-based systems - Characteristics of AI and ML systems - AI-specific quality characteristics |
| Topic 3: Test Levels and Machine Learning Systems | 15% | - Test strategies for AI projects - Unit, integration, system, and acceptance testing for ML - Testing across ML lifecycle stages |
| Topic 4: Machine Learning Development Testing | 10% | - Regression testing for retrained models - Testing MLOps and deployment pipelines - Testing ML development workflows |
| Topic 5: Testing Generative AI and Large Language Models | 15% | - Generative AI fundamentals - Evaluation of generative AI outputs - Specific risks and test approaches for LLMs |
| Topic 6: Model Testing for Machine Learning Systems | 20% | - Drift detection and monitoring - Adversarial and robustness testing - Model performance and accuracy testing - Metamorphic and statistical testing - Explainability and transparency testing |
| Topic 7: Test Approaches and Techniques for AI Systems | 10% | - Test environment and tool considerations - Risk-based testing - Exploratory testing and red teaming - A/B testing and back-to-back testing |
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NEW QUESTION # 74
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?
Answer: C
Explanation:
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:
NEW QUESTION # 75
Which statement about the property of the test environment for an AI-based system is correct?
Answer: C
Explanation:
The ISTQB CT-AI syllabus (Section4.3 - Test Environments for AI Systems) describes that, unlike conventional software testing, testing AI systems may require specialized toolsfor analyzing and explaining the decisions of ML models. This includes visualization tools, explainability frameworks, and diagnostic utilities to understand why the AI made a certain prediction. Since AI decisions may be non-transparent, the test environment must supportexplainability, making Option B correct.
NEW QUESTION # 76
Which of the below is NOT a characteristic that makes it difficult to use AI based systems?
Answer: A
Explanation:
The correct answer is B. Lack of standards . In the CT-AI syllabus discussion of safety and AI, the listed characteristics that make it more difficult to ensure AI-based systems are safe include complexity , non- determinism , probabilistic nature , self-learning , lack of transparency, interpretability and explainability , and lack of robustness . Therefore, options A, C, and D are directly aligned with the syllabus list.
"Lack of standards" may be relevant to governance, regulation, or organizational maturity, but it is not one of the syllabus-listed intrinsic characteristics that makes AI-based systems difficult to use in safety-related applications. The syllabus separately discusses standards and regulations in Chapter 1, including ISO/IEC work, GDPR, automotive safety standards, and related regulatory contexts. However, this does not make
"lack of standards" the correct answer for this specific question. The question is targeting the safety-related AI characteristics in Chapter 2, not the broader standards landscape.
References/topics: CT-AI Syllabus Chapter 2, Section 2.8 "Safety and AI."
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NEW QUESTION # 77
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network the shortest path indicates a buy, and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least 0.75%.
Which of the following statements BEST explains the type of coverage being tested on the neural network?
Answer: C
Explanation:
Threshold coverageis a specific type of coverage measure used in neural network testing. It ensures that each neuron in the network achieves an activation value greater than a specified threshold. This is particularly relevant to the scenario described, where testers verify that neurons activate only when the future value of the commodity exceeds the spot price by at least0.75%.
* Threshold-based activation:The test case in the question isexplicitly verifying whether neurons activate only when a certain threshold (0.75%) is exceeded.This aligns perfectly with the definition ofthreshold coverage.
* Common in Neural Network Testing:Threshold coverage is used to measurewhether each neuron in a neural network reaches a specified activation value, ensuring that the neural network behaves as expected when exposed to different test inputs.
* Precedent in Research:TheDeepXplore frameworkused a threshold of0.75%to identify incorrect behaviors in neural networks, making this coverage criterion well-documented in AI testing research.
* (B) Neuron Coverage#
* Neuron coverageonly checks whether a neuron activates (non-zero value)at some point during testing. It does not consider specific activation thresholds, making it less precise for this scenario.
* (C) Sign-Change Coverage#
* This coverage measures whether each neuron exhibitsboth positive and negative activation values, which isnot relevant to the given scenario(where activation only matters when exceeding a specific threshold).
* (D) Value-Change Coverage#
* This coverage requires each neuron to producetwo activation values that differ by a chosen threshold, but the question focuses onwhether activation occurs beyond a fixed threshold, not changes in activation values.
* Threshold coverage ensures that neurons exceed a given activation threshold"Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold. The researchers who created the DeepXplore framework suggested neuron coverage should be measured based on an activation value exceeding a threshold, changing based on the situation." Why is Threshold Coverage Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asthreshold coverage ensures the neural network's activation is correctly evaluated based on the required condition (0.75%).
NEW QUESTION # 78
Which of the following statements about reinforcement learning is correct?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
Section1.6.3 - Reinforcement Learningof the ISTQB CT-AI syllabus states that reinforcement learning (RL) is based on anagent interacting with an environment, performing actions, and receivingrewards or penalties.
The core concept is thereward function, which guides the agent's learning process. The syllabus emphasizes that training in RL isdriven by rewards, and the agent aims to maximize cumulative reward over time.
Therefore, OptionCdirectly reflects the correct description: the agent learns by being rewarded for successful actions .
Option A is incorrect because RL doesnotuse labeled data; that applies to supervised learning. Option B contradicts the syllabus definition: RL fundamentallyrequiresinteraction with the environment. Option D is incorrect because the reward function isdefined by humans, not learned by the agent; the agent learns apolicy, not the reward function itself.
Thus, OptionCis the only statement consistent with RL as defined in the syllabus.
NEW QUESTION # 79
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