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| Section | Weight | Objectives |
|---|---|---|
| Testing AI-Specific Quality Characteristics | 20% | - Fairness and Bias Detection - Explainability and Interpretability - Accuracy, Precision, Recall, F1-Score - Robustness Testing |
| 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 |
| Basics of AI | 8% | - Definitions and Terminology - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - AI Types and Techniques - Data Concepts (Training, Validation, Test Data) |
| Practical Considerations | 12% | - Test Environment Setup for AI - Test Data Quality and Preparation - Documentation and Reporting - Automation in AI Testing |
| AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Test Oracle Techniques for AI - Adversarial Testing - Metamorphic Testing |
| Neural Networks and Deep Learning | 20% | - Testing Deep Learning Models - Neural Network Architecture Basics - Model Training and Evaluation Metrics - Overfitting and Underfitting |
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NEW QUESTION # 144
Which of the following is a technique used in machine learning?
Answer: D
Explanation:
Decision trees are a foundational algorithm used in supervised machine learning. The syllabus describes:
"A decision tree is a tree-like ML model whose nodes represent decisions and whose branches represent possible outcomes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.4)
NEW QUESTION # 145
Which machine learning approach is most suitable for predicting customer purchase probability?
Choose ONE option (1 out of 4)
Answer: D
Explanation:
The ISTQB CT-AI syllabus explains in Section1.6 - Machine Learning Approachesthatsupervised learningis appropriate when labeled data exists and the goal is to predict an output based on known historical examples.
Predicting a customer'spurchase probabilityis aclassificationtask when the output corresponds to discrete categories such as"likely to purchase"vs."not likely to purchase."The syllabus gives similar examples in describing classification as the process of assigning instances to predefined classes based on learned patterns in labeled data. Because the retail company wants to determine whether a customer will make a purchase based on marketing actions, classification is the most appropriate choice .
Option A (regression) predicts continuous numeric values and is less suitable because the task centers oncategorical likelihood, not estimating exact monetary values. Option C (unsupervised learning) is used when labels are not available-here, the company has labeled purchase histories. Option D (reinforcement learning) requires an interactive environment with reward-driven behavior, which is not applicable to this scenario.
Thus,supervised learning (classification)is the most suitable approach according to the syllabus.
NEW QUESTION # 146
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it fails and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known healthy engines and ones which experienced a catastrophic failure due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.
What is the precision of this predictive model?
Answer: A
Explanation:
The syllabus defines precision as:
"Precision = TP / (TP + FP) * 100%. Precision measures the proportion of positives that were correctly predicted." Using the confusion matrix:
* TP = 90
* FP = 10Thus: Precision = (90 / (90 + 10)) * 100% = 90 / 100 * 100% = 90%However, the confusion matrix totals suggest that the calculation should be done in the form:Precision = 90 / (90 + 10) * 100%
= 90%Since the given answers do not include exactly 90%, the closest approximation and the correct answer, as described in the syllabus, would be 90%.(Reference: ISTQB CT-AI Syllabus v1.0, Section
5.1, page 40 of 99)
NEW QUESTION # 147
Which statement about automation bias is correct?
Answer: B
Explanation:
Automation bias is defined in Section4.4 - Human Factors in AI Testingof the ISTQB CT-AI syllabus. It refers to the human tendency to overly trust, rely on, or defer to automated system outputs. The syllabus explains that this bias arises especially indecision-support systems, where humans may accept AI judgments without adequate verification. This aligns directly with Option B).
NEW QUESTION # 148
Which ONE of the following statements about the hardware used to implement ML systems is MOST likely to be correct?
Answer: B
Explanation:
ML systems often require hardware that supports complex operations, such as matrix multiplications and other linear algebra computations. These operations are fundamental to many machine learning algorithms, particularly in deep learning. Specialist hardware (e.g., GPUs) may be used for efficiency, but complex operations are the core requirement.
NEW QUESTION # 149
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