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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basics of AI | 8% | - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Definitions and Terminology - Data Concepts (Training, Validation, Test Data) - AI Types and Techniques |
| Topic 2: Testing AI-Based Systems | 20% | - Testing AI-Specific Quality Characteristics - Challenges in Testing AI-Based Systems - Bias and Fairness Considerations - Test Levels for AI-Based Systems |
| Topic 3: Practical Considerations | 12% | - Documentation and Reporting - Automation in AI Testing - Test Data Quality and Preparation - Test Environment Setup for AI |
| Topic 4: AI Test Methods and Techniques | 20% | - Test Oracle Techniques for AI - Black-Box Testing for AI Systems - Metamorphic Testing - Adversarial Testing |
| Topic 5: Neural Networks and Deep Learning | 20% | - Model Training and Evaluation Metrics - Overfitting and Underfitting - Neural Network Architecture Basics - Testing Deep Learning Models |
| Topic 6: Testing AI-Specific Quality Characteristics | 20% | - Robustness Testing - Explainability and Interpretability - Accuracy, Precision, Recall, F1-Score - Fairness and Bias Detection |
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NEW QUESTION # 55
"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
Answer: C
Explanation:
* A. The difficulty of defining criteria for improvement before the model can be accepted.
* Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.
* B. The fast pace of change did not allow sufficient time for testing.
* This can significantly affect the model's performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.
* C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.
* D. There was an algorithmic bias in the AI system.
* Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions.
Given the context of the self-learning nature and the need for real-time adaptability, optionAis least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.
NEW QUESTION # 56
The training of an ML model... What type of bias is LEAST important to look for when testing the model?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
The ISTQB CT-AI syllabus distinguishes between several types of bias relevant in AI testing, including sample bias,algorithmic bias, andinappropriate bias. In Section3.3 - Bias in AI-Based Systems, the syllabus stresses the importance of identifying biases that originate fromtraining data,model development, anddecision logic. Sample bias occurs when the training data does not adequately represent the population; algorithmic bias arises when the model produces systematically skewed results due to learned patterns; inappropriate bias involves ethically or socially problematic distortions in the outcomes. All three of these bias types directly affect theoutputs of the AI modeland are therefore highly relevant when testing an industrial inspection system intended to reliably detect defects. These biases can lead to defective items being missed or false alarms being raised, which impacts quality assurance significantly .
Automation bias, however, is fundamentally different. It refers to ahuman cognitive bias, where users (e.g., inspectors) overly trust or rely on the AI system's output. While important in user-interaction testing, it isnota biaswithin the ML model itself. Since the question asks which bias isleast important when testing the model, automation bias can be legitimately deprioritized duringmodel-level testing. Therefore, OptionBis correct.
NEW QUESTION # 57
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
Answer: D
Explanation:
Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
NEW QUESTION # 58
Before deployment of an AI based system, a developer is expected to demonstrate in a test environment how decisions are made. Which of the following characteristics does decision making fall under?
Answer: B
Explanation:
The syllabus definesexplainabilityas the ability to understand how the AI-based system comes up with a particular result:
"Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result."
NEW QUESTION # 59
The training of an ML model... What type of bias is LEAST important to look for when testing the model?
Answer: B
Explanation:
The ISTQB CT-AI syllabus distinguishes between several types of bias relevant in AI testing, includingsample bias,algorithmic bias, andinappropriate bias. In Section3.3 - Bias in AI-Based Systems, the syllabus stresses the importance of identifying biases that originate fromtraining data,model development, anddecision logic. Sample bias occurs when the training data does not adequately represent the population; algorithmic bias arises when the model produces systematically skewed results due to learned patterns; inappropriate bias involves ethically or socially problematic distortions in the outcomes. All three of these bias types directly affect theoutputs of the AI modeland are therefore highly relevant when testing an industrial inspection system intended to reliably detect defects. These biases can lead to defective items being missed or false alarms being raised, which impacts quality assurance significantly .
Automation bias, however, is fundamentally different. It refers to ahuman cognitive bias, where users (e.g., inspectors) overly trust or rely on the AI system's output. While important in user- interaction testing, it isnota biaswithin the ML model itself. Since the question asks which bias isleast important when testing the model, automation bias can be legitimately deprioritized duringmodel-level testing. Therefore, Option B is correct.
NEW QUESTION # 60
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