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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Test Methods and Techniques | 20% | - Adversarial Testing - Metamorphic Testing - Black-Box Testing for AI Systems - Test Oracle Techniques for AI |
| Topic 2: Testing AI-Specific Quality Characteristics | 20% | - Accuracy, Precision, Recall, F1-Score - Explainability and Interpretability - Fairness and Bias Detection - Robustness Testing |
| Topic 3: Testing AI-Based Systems | 20% | - Bias and Fairness Considerations - Test Levels for AI-Based Systems - Challenges in Testing AI-Based Systems - Testing AI-Specific Quality Characteristics |
| Topic 4: Neural Networks and Deep Learning | 20% | - Model Training and Evaluation Metrics - Neural Network Architecture Basics - Testing Deep Learning Models - Overfitting and Underfitting |
| Topic 5: Practical Considerations | 12% | - Automation in AI Testing - Test Environment Setup for AI - Test Data Quality and Preparation - Documentation and Reporting |
| Topic 6: Basics of AI | 8% | - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - AI Types and Techniques - Definitions and Terminology - Data Concepts (Training, Validation, Test Data) |
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質問 # 121
A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not.
The bank has enough data on past customers to randomly split the data into a training data set and a test
/validation data set. A logistic regression model is constructed on the training data set using the following independent variables:
Gender
Marital status
Number of dependents
Education
Income
Loan amount
Loan term
Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?
正解：A
解説：
Bias in an AI system occurs when the training data contains inherent prejudices that cause the model to make unfair predictions. Experience-based testing, particularlyExploratory Data Analysis (EDA), helps uncover these biases by analyzing patterns, distributions, and potential discriminatory factors in the training data.
* Option A:"Experience-based testing should be used to confirm that the training data set is operationally relevant. This can include applying exploratory data analysis (EDA) to check for bias within the training data set."
* This is the correct answer. EDA involves examining the dataset for bias, inconsistencies, or missing values, ensuring fairness in ML model predictions.
* Option B:"Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set. If the two models significantly differ, it will indicate there is bias in the original model."
* Back-to-back testing is used for regression testing and to compare versions of an AI system but is not primarily used to detect bias.
* Option C:"Acceptance testing should be used to make sure the algorithm is suitable for the customer.
The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation data set ensuring no bias is present."
* Acceptance testing focuses on meeting predefined business requirements rather than detecting and mitigating bias.
* Option D:"A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model."
* A/B testing is used for evaluating variations of a model rather than for explicitly identifying bias.
* Bias Testing Methods:"AI-based systems should be tested for algorithmic bias, sample bias, and inappropriate bias. Experience-based testing and EDA are useful for detecting bias".
* Exploratory Data Analysis (EDA):"EDA helps uncover potential bias in training data through visualization and statistical analysis".
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,Option A is the best choice for detecting bias in the loan applicant model.
質問 # 122
Which ONE of the following options for a test basis would give the LEAST coverage when using AI based test generation?
正解：D
解説：
The correct answer is C. A pseudo-oracle . The syllabus states that AI-based test generation can use sources such as source code, the user interface, and a machine-readable test model . Some tools also generate tests from low-level system behaviour observed through instrumentation or log files. A test model describing required behaviour is particularly strong because it can guide functional coverage and expected behaviour.
A list of web pages may provide structural coverage of the web application, though it is weaker than a behavioural model. An XML schema may also support systematic generation of valid and invalid structured data tests. A pseudo-oracle, however, is not a strong input for generating test coverage. Its role is to help decide whether a result is acceptable, typically by comparing the SUT's output with another system. The syllabus explicitly presents pseudo-oracles as a solution to the test oracle problem, not as a primary coverage- oriented test basis. Therefore, it would provide the least coverage for AI-based test generation.
References/topics: CT-AI Syllabus Chapter 11, Section 11.3 "Using AI for Test Case Generation"; Chapter 9, Section 9.3 "Back-to-Back Testing."
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質問 # 123
Which of the following options BEST describes an example of reinforcement learning?
正解：D
解説：
The correct answer is A . Reinforcement learning is described in the CT-AI syllabus as an approach in which an intelligent agent learns by interacting with its environment iteratively and learning from experience. The agent receives rewards for correct decisions and penalties for incorrect decisions. The syllabus also identifies robotics, autonomous vehicles, and chatbots as application examples for reinforcement learning.
A house-cleaning robot that adjusts its path based on route efficiency and dirt removed fits this pattern closely. It acts in an environment, observes results, and improves its behaviour according to an implicit reward such as efficient coverage and cleaning effectiveness. Option B may involve optimization, but the description is less clearly framed around agent-environment interaction and reward-based trial learning.
Option C describes data acquisition for supervised or self-improving image recognition, not reinforcement learning. Option D describes predictive modelling based on historical labelled or structured data, which is more consistent with supervised learning.
References/topics: CT-AI Syllabus Chapter 3, Section 3.1.3 "Reinforcement Learning."
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質問 # 124
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
正解：A
解説：
Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.
AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.
AI systems require changing operational environments; therefore, flexibility is required (B): While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.
Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.
Self-learning systems are expected to deal with new situations without explicitly having to program for it (D): This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.
Hence, the correct answer is C. Flexible AI systems allow for easier modification of the system as a whole.
質問 # 125
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?
正解：D
解説：
The syllabus defines clustering as:
"Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.2, page 26 of 99)
質問 # 126
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