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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basics of AI | 8% | - AI Types and Techniques - Definitions and Terminology - Data Concepts (Training, Validation, Test Data) - Machine Learning Types (Supervised, Unsupervised, Reinforcement) |
| Topic 2: Testing AI-Specific Quality Characteristics | 20% | - Fairness and Bias Detection - Accuracy, Precision, Recall, F1-Score - Explainability and Interpretability - Robustness Testing |
| Topic 3: Practical Considerations | 12% | - Test Data Quality and Preparation - Documentation and Reporting - Test Environment Setup for AI - Automation in AI Testing |
| Topic 4: Neural Networks and Deep Learning | 20% | - Overfitting and Underfitting - Neural Network Architecture Basics - Testing Deep Learning Models - Model Training and Evaluation Metrics |
| Topic 5: AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Test Oracle Techniques for AI - Adversarial Testing - Metamorphic Testing |
| Topic 6: Testing AI-Based Systems | 20% | - Testing AI-Specific Quality Characteristics - Challenges in Testing AI-Based Systems - Test Levels for AI-Based Systems - Bias and Fairness Considerations |
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NEW QUESTION # 29
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 dataset and a test
/validation dataset. A logistic regression model is constructed on the training dataset 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?
Answer: A
Explanation:
The syllabus mentions that experience-based testing and EDA are effective for detecting biases:
"Experience-based testing can be used to verify that the training dataset is operationally relevant and identify potential sources of bias. EDA is also useful for exploring the data and understanding any relationships that might lead to bias in the model." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.3, page 58 of 99)
NEW QUESTION # 30
Which ONE of the following statements is MOST true about black box adversarial testing?
Answer: D
Explanation:
The correct answer is B . The CT-AI syllabus explains that black-box adversarial attacks involve exploring the model to determine its functionality and then building a duplicate model with similar functionality. The attacker then uses a white-box approach on the duplicate model to identify adversarial examples. Because adversarial examples are generally transferable, those examples will normally also work on the original model.
Option A describes white-box adversarial testing, where the algorithm, model settings, and parameters are known. Option C describes data poisoning , where training data is manipulated to induce incorrect future predictions or create vulnerabilities. Option D describes pairwise or broader combinatorial testing, which is used where many parameters and values make exhaustive testing impractical. Black-box adversarial testing is instead based on limited knowledge of the target model and the empirical transferability of adversarial examples. If a duplicate model cannot be built, the syllabus also notes that high-volume automated testing may be used to discover adversarial examples and observe results.
References/topics: CT-AI Syllabus Chapter 9, Section 9.1.1 "Adversarial Attacks"; Section 9.1.2 "Data Poisoning."
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NEW QUESTION # 31
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
The difficulty of defining criteria for improvement before the model can be accepted.
The fast pace of change did not allow sufficient time for testing.
The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
There was an algorithmic bias in the Al system.
Answer: C
Explanation:
Given the context of the self-learning nature and the need for real-time adaptability, option A is least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.
NEW QUESTION # 32
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: A
Explanation:
The syllabus defines reward hacking as:
"Reward hacking can result from an AI-based system achieving a specified goal by using a 'clever' or 'easy' solution that perverts the spirit of the designer's intent." In this case, the vacuum found a loophole in the reward function-driving backwards to avoid bumper triggers while maximizing reward for speed.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.6, page 24 of 99)
NEW QUESTION # 33
An image classification system is being trained for classifying faces of humans. The distribution of the data is
70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?
SELECT ONE OPTION
Answer: C
Explanation:
* A. This is an example of expert system bias.
* Expert system bias refers to bias introduced by the rules or logic defined by experts in the system, not by the data distribution.
* B. This is an example of sample bias.
* Sample bias occurs when the training data is not representative of the overall population that the model will encounter in practice. In this case, the over-representation of ethnicity A (70%) compared to B, C, and D (30%) creates a sample bias, as the model may become biased towards better performance on ethnicity A.
* C. This is an example of hyperparameter bias.
* Hyperparameter bias relates to the settings and configurations used during the training process, not the data distribution itself.
* D. This is an example of algorithmic bias.
* Algorithmic bias refers to biases introduced by the algorithmic processes and decision-making rules, not directly by the distribution of training data.
Based on the provided information, optionB(sample bias) best describes the situation because the training data is skewed towards ethnicity A, potentially leading to biased model performance.
NEW QUESTION # 34
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