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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basics of AI | 8% | - Definitions and Terminology - AI Types and Techniques - Data Concepts (Training, Validation, Test Data) - Machine Learning Types (Supervised, Unsupervised, Reinforcement) |
| Topic 2: AI Test Methods and Techniques | 20% | - Test Oracle Techniques for AI - Black-Box Testing for AI Systems - Adversarial Testing - Metamorphic Testing |
| Topic 3: Testing AI-Based Systems | 20% | - Test Levels for AI-Based Systems - Bias and Fairness Considerations - Testing AI-Specific Quality Characteristics - Challenges in Testing AI-Based Systems |
| Topic 4: Practical Considerations | 12% | - Automation in AI Testing - Test Data Quality and Preparation - Test Environment Setup for AI - Documentation and Reporting |
| Topic 5: Neural Networks and Deep Learning | 20% | - Overfitting and Underfitting - Testing Deep Learning Models - Neural Network Architecture Basics - Model Training and Evaluation Metrics |
| Topic 6: Testing AI-Specific Quality Characteristics | 20% | - Fairness and Bias Detection - Accuracy, Precision, Recall, F1-Score - Explainability and Interpretability - Robustness Testing |
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NEW QUESTION # 99
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION
Answer: C
Explanation:
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 isC. Flexible AI systems allow for easier modification of the system as a whole.
ISTQB CT-AI Syllabus Section 2.1 on Flexibility and Adaptability discusses the importance of flexibility in AI systems and how it enables easier modification and adaptability to new situations.
Sample Exam Questions document, Question #30 highlights the importance of flexibility in AI systems.
NEW QUESTION # 100
Which ONE of the below types of testing is NOT a type of experience based testing applied to an AI based system?
Answer: B
Explanation:
The correct answer is B . The CT-AI syllabus identifies experience-based testing as including error guessing, exploratory testing, and checklist-based testing , all of which may be applied to AI-based systems. It also explicitly relates exploratory testing to exploratory data analysis, where data is examined for patterns, relationships, trends, outliers, distribution, format, and ranges.
Option A matches exploratory testing and EDA, because the tester explores training data to guide subsequent testing. Option C matches error guessing, because the tester uses knowledge of previous ML-system failures, such as biased training data, to identify likely faults. Option D matches checklist-based testing, because the syllabus cites Google's ML test checklist as an effective checklist approach for ML systems.
Option B is not, by itself, an experience-based AI testing type. Usability testing may be important for AI- based systems, especially where users consume predictions, recommendations, or explanations, but it is classified as a quality-characteristic or user-experience concern, not as one of the named experience-based techniques in this syllabus context.
References/topics: CT-AI Syllabus Chapter 9, Section 9.6 "Experience-Based Testing of AI-Based Systems"; Section 9.6.1 "Exploratory Testing and EDA."
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NEW QUESTION # 101
Which of the following options is an example of the concept of overfitting?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
The ISTQB CT-AI syllabus definesoverfittingin Section3.2 - ML Model Evaluationas a condition where an ML model learns the training data too precisely-including noise and irrelevant detail-resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. OptionAperfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from other universities. This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across environments .
Option B instead describessample biasor inadequate training diversity, not overfitting. Option C involves transfer learningor model extension, unrelated to overfitting. Option D indicatesinsufficient training data qualityor lack of meaningful features, but not overfitting. Only Option A reflects the syllabus definition directly: overly specialized training leading to reduced predictive performance on new data.
Thus,Ais the correct and syllabus-aligned example of overfitting.
NEW QUESTION # 102
Which characteristic of AI-based systems makes it difficult to ensure they are safe (e.g., not harming humans)?
Choose ONE option (1 out of 4)
Answer: A
Explanation:
The ISTQB CT-AI syllabus lists several characteristics that make it difficult to ensure safety in AI-based systems. Section2.8 - Safety and AIexplicitly names the characteristics that complicate safety assurance:
complexity,non-determinism,probabilistic behavior,self-learning,lack of transparency, andlack of robustness. Among these,complexityis a core challenge because modern AI systems-particularly those using deep learning-have highly non-linear behavior, large numbers of parameters, and intricate interactions that are hard to predict.
Option B (Complexity) directly aligns with the syllabus and is therefore correct.
Option A (Determinism) is the opposite of AI behavior; AI is oftennon-deterministic, and determinism does notmake systems unsafe. Option C (Interpretability) does impact trust and explainability, but the syllabus positions it as a transparency challenge, not the primary difficulty in ensuring safety. Option D (Robustness) is a desired quality, not a reason safety is hard; alackof robustness would be a challenge, not robustness itself.
Thus,complexitybest reflects the syllabus' explicit safety-related difficulty.
NEW QUESTION # 103
Which ONE of the following statements is true about dynamic testing for inappropriate bias?
Answer: B
NEW QUESTION # 104
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