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| Section | Weight | Objectives |
|---|---|---|
| Practical Considerations | 12% | - Test Environment Setup for AI - Automation in AI Testing - Test Data Quality and Preparation - Documentation and Reporting |
| Basics of AI | 8% | - Definitions and Terminology - AI Types and Techniques - Data Concepts (Training, Validation, Test Data) - Machine Learning Types (Supervised, Unsupervised, Reinforcement) |
| Neural Networks and Deep Learning | 20% | - Model Training and Evaluation Metrics - Neural Network Architecture Basics - Testing Deep Learning Models - Overfitting and Underfitting |
| Testing AI-Specific Quality Characteristics | 20% | - Robustness Testing - Fairness and Bias Detection - Explainability and Interpretability - Accuracy, Precision, Recall, F1-Score |
| AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Metamorphic Testing - Test Oracle Techniques for AI - Adversarial Testing |
| Testing AI-Based Systems | 20% | - Challenges in Testing AI-Based Systems - Bias and Fairness Considerations - Test Levels for AI-Based Systems - Testing AI-Specific Quality Characteristics |
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NEW QUESTION # 155
Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.
Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?
SELECT ONE OPTION
Answer: D
Explanation:
Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options is least likely to be a reason for the explosion in the number of parameters.
Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car's system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.
Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car's sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.
ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.
Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.
Hence, the least likely reason for the incredible growth in the number of parameters is C. ML model metrics to evaluate the functional performance.
Reference:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing discusses the application of this technique to manage the combinations of different variables in AI-based systems, including those used in self-driving cars.
Sample Exam Questions document, Question #29 provides context for the explosion in parameter combinations in self-driving cars and highlights the use of pairwise testing as a method to manage this complexity.
NEW QUESTION # 156
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
Answer: D
Explanation:
This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
NEW QUESTION # 157
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?
Answer: A
Explanation:
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.
NEW QUESTION # 158
Which of the following is a problem with AI-generated test cases that are generated from the requirements?
Answer: B
Explanation:
AI-generated test cases are often created using machine learning (ML) models or heuristic algorithms. While these can be effective in generating large numbers of test cases quickly, they often suffer from the "test oracle problem." Test Oracle Problem: A test oracle is the mechanism used to determine the expected output of a test case. AI-generated test cases often lack expected results because AI-based tools do not inherently understand what the correct output should be.
Difficulty in Verification: Without expected results, verifying test cases becomes challenging.
Testers must rely on heuristics, anomaly detection, or significant failures, rather than traditional pass/fail conditions.
NEW QUESTION # 159
You have been asked for your opinion on the ML approach to be used for a new age verification system. A training dataset is available with many pictures of faces and the subject's age.
When new users sign-up to a mobile app, they have to take a picture of their face. If the system believes they are under 18 years old it will ask them to provide a picture of their passport to prove their age.
Which ONE of the following approaches do you expect to be MOST likely to succeed?
Answer: A
Explanation:
A supervised learning classification system is most likely to succeed in this scenario because it involves using labeled data (the pictures of faces with corresponding ages) to train a model. The system can then classify whether a person is under or over 18 years old. The approach of re- training based on new data (including whether they later provided ID showing their age) ensures continuous improvement of the model. This method directly aligns with the goal of age verification.
NEW QUESTION # 160
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