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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Practical Considerations | 12% | - Test Data Quality and Preparation - Automation in AI Testing - Test Environment Setup for AI - Documentation and Reporting |
| Topic 2: Testing AI-Specific Quality Characteristics | 20% | - Accuracy, Precision, Recall, F1-Score - Fairness and Bias Detection - Robustness Testing - Explainability and Interpretability |
| Topic 3: Basics of AI | 8% | - AI Types and Techniques - Data Concepts (Training, Validation, Test Data) - Definitions and Terminology - Machine Learning Types (Supervised, Unsupervised, Reinforcement) |
| Topic 4: Neural Networks and Deep Learning | 20% | - Testing Deep Learning Models - Model Training and Evaluation Metrics - Overfitting and Underfitting - Neural Network Architecture Basics |
| Topic 5: AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Test Oracle Techniques for AI - Metamorphic Testing - Adversarial Testing |
| Topic 6: Testing AI-Based Systems | 20% | - Testing AI-Specific Quality Characteristics - Bias and Fairness Considerations - Test Levels for AI-Based Systems - Challenges in Testing AI-Based Systems |
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NEW QUESTION # 66
A transportation company operates three types of delivery vehicles in its fleet. The vehicles operate at different speeds (slow, medium, and fast). The transportation company is attempting to optimize scheduling and has created an AI-based program to plan routes for its vehicles using records from the medium-speed vehicle traveling to selected destinations. The test team uses this data in metamorphic testing to test the accuracy of the estimated travel times created by the AI route planner with the actual routes and times.
Which of the following describes the next phase of metamorphic testing?
Answer: A
Explanation:
Metamorphic Testing (MT)is a testing technique that verifies AI-based systems by generatingfollow-up test casesbased on existing test cases. These follow-up test cases adhere to aMetamorphic Relation (MR), ensuring that if the system is functioning correctly, changes in input should result in predictable changes in output.
* Metamorphic testing works by transforming source test cases into follow-up test cases
* Here, thesource test caseinvolves testing themedium-speed vehicle'stravel time.
* Thefollow-up test casesare derived byextrapolating travel times for fast and slow vehiclesusing predictable relationships based on speed differences.
* MR states that modifying input should result in a predictable change in output
* Since the speed of the vehicle is a known factor, it is possible to predict the new arrival times and verify whether they follow expected trends.
* This is a direct application of metamorphic testing principles
* Inroute optimization systems, metamorphic testing often applies transformations tospeed, distance, or conditionsto verify expected outcomes.
* (B) Decomposing each route into traffic density and vehicle power#
* While useful for statistical analysis, this approach does not generate follow-up test cases based on a definedmetamorphic relation (MR).
* (C) Selecting dissimilar routes and transforming them into a fast or slow route#
* Thisdoes not follow metamorphic testing principles, which require predictable transformations.
* (D) Running fast vehicles on long routes and slow vehicles on short routes#
* This methoddoes not maintain a controlled MRand introduces too manyuncontrolled variables.
* Metamorphic testing generates follow-up test cases based on a source test case."MT is a technique aimed at generating test cases which are based on a source test case that has passed.One or more follow- up test cases are generated by changing (metamorphizing) the source test case based on a metamorphic relation (MR)."
* MT has been used for testing route optimization AI systems."In the area of AI, MT has been used for testing image recognition, search engines, route optimization and voice recognition, among others." Why Option A is Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as it aligns with the principles ofmetamorphic testing by modifying input speeds and verifying expected results.
NEW QUESTION # 67
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
Answer: C
NEW QUESTION # 68
Which ONE of the following is a factor associated with the test data that can create challenges specific to testing AI-based systems?
Answer: C
Explanation:
Obtaining appropriate data from users is a significant challenge when testing AI-based systems, as user-provided data may be noisy, unstructured, or highly variable. This can introduce complexities in testing the AI model's robustness and generalizability.
NEW QUESTION # 69
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 # 70
Which of the following describes the AI effect?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
TheAI Effectis clearly defined in theISTQB Certified Tester AI Testing Syllabus v1.0under Section1.1 - Definition of AI and AI Effect. The document explains that society's understanding of what qualifies as "AI" changes over time. Technologies once considered AI-such as expert systems from the 1970s and 1980s or early chess-playing systems-are no longer viewed as AI today. This phenomenon is explicitly labeled the"AI Effect,"described as"the changing perception of what constitutes AI."The syllabus states that as AI capabilities become routine or widely implemented, they often stop being perceived as true artificial intelligence .
Options B, C, and D do not capture this definition. While AI learning from data (B) is a property of ML, it does not describe the shifting perception of AI. Option C describes a technological achievement, not a perceptual shift. Option D references a historical AI milestone (Deep Blue defeating Kasparov) that the syllabus specifically uses as an example of technology that is no longer considered AI due to the AI Effect.
Therefore, onlyOption Aaccurately reflects the AI Effect as defined by the ISTQB syllabus.
NEW QUESTION # 71
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