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| Section | Weight | Objectives |
|---|---|---|
| Testing AI-Specific Quality Characteristics | 20% | - Fairness and Bias Detection - Robustness Testing - Accuracy, Precision, Recall, F1-Score - Explainability and Interpretability |
| Neural Networks and Deep Learning | 20% | - Neural Network Architecture Basics - Overfitting and Underfitting - Testing Deep Learning Models - Model Training and Evaluation Metrics |
| 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 |
| AI Test Methods and Techniques | 20% | - Adversarial Testing - Test Oracle Techniques for AI - Black-Box Testing for AI Systems - Metamorphic Testing |
| Practical Considerations | 12% | - Test Environment Setup for AI - Automation in AI Testing - Test Data Quality and Preparation - Documentation and Reporting |
| Basics of AI | 8% | - AI Types and Techniques - Data Concepts (Training, Validation, Test Data) - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Definitions and Terminology |
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NEW QUESTION # 23
Which of the below is NOT a characteristic that makes it difficult to use AI based systems?
Answer: A
Explanation:
The correct answer is B. Lack of standards . In the CT-AI syllabus discussion of safety and AI, the listed characteristics that make it more difficult to ensure AI-based systems are safe include complexity , non- determinism , probabilistic nature , self-learning , lack of transparency, interpretability and explainability , and lack of robustness . Therefore, options A, C, and D are directly aligned with the syllabus list.
"Lack of standards" may be relevant to governance, regulation, or organizational maturity, but it is not one of the syllabus-listed intrinsic characteristics that makes AI-based systems difficult to use in safety-related applications. The syllabus separately discusses standards and regulations in Chapter 1, including ISO/IEC work, GDPR, automotive safety standards, and related regulatory contexts. However, this does not make
"lack of standards" the correct answer for this specific question. The question is targeting the safety-related AI characteristics in Chapter 2, not the broader standards landscape.
References/topics: CT-AI Syllabus Chapter 2, Section 2.8 "Safety and AI."
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NEW QUESTION # 24
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION
Answer: A
Explanation:
Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.
Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.
Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.
Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.
Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.
Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters is C. Tuning the model.
Reference:
ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set.
Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.
NEW QUESTION # 25
ln the near future, technology will have evolved, and Al will be able to learn multiple tasks by itself without needing to be retrained, allowing it to operate even in new environments. The cognitive abilities of Al are similar to a child of 1-2 years.' In the above quote, which ONE of the following options is the correct name of this type of Al?
SELECT ONE OPTION
Answer: A
Explanation:
* A. Technological singularity
Technological singularity refers to a hypothetical point in the future when AI surpasses human intelligence and can continuously improve itself without human intervention. This scenario involves capabilities far beyond those described in the question.
* B. Narrow AI
Narrow AI, also known as weak AI, is designed to perform a specific task or a narrow range of tasks. It does not have general cognitive abilities and cannot learn multiple tasks by itself without retraining.
* C. Super AI
Super AI refers to an AI that surpasses human intelligence and capabilities across all fields. This is an advanced concept and not aligned with the description of having cognitive abilities similar to a young child.
* D. General AI
General AI, or strong AI, has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human cognitive abilities. It aligns with the description of AI that can learn multiple tasks and operate in new environments without needing retraining.
NEW QUESTION # 26
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?
Answer: C
Explanation:
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1)
NEW QUESTION # 27
Which of the following statements about explainable AI is correct?
Answer: A
Explanation:
Section2.10 - Explainability and Transparency of the ISTQB CT-AI syllabus describes explain able AI as the ability of a system to provide human-understandable insight into its decisions. The syllabus references The Royal Society's reportas a foundational source explaining why explainability is important. Among the stated motivations is the need toincrease user trust and confidencein AI systems by making their decisions understandable and justifiable. Therefore, Option C directly reflects the syllabus content .
NEW QUESTION # 28
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