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| Section | Weight | Objectives |
|---|---|---|
| Practical Considerations | 12% | - Automation in AI Testing - Test Environment Setup for AI - Documentation and Reporting - Test Data Quality and Preparation |
| Testing AI-Based Systems | 20% | - Bias and Fairness Considerations - Testing AI-Specific Quality Characteristics - Test Levels for AI-Based Systems - Challenges in Testing AI-Based Systems |
| Basics of AI | 8% | - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - AI Types and Techniques - Definitions and Terminology - Data Concepts (Training, Validation, Test Data) |
| AI Test Methods and Techniques | 20% | - Metamorphic Testing - Test Oracle Techniques for AI - Black-Box Testing for AI Systems - Adversarial Testing |
| Neural Networks and Deep Learning | 20% | - Overfitting and Underfitting - Neural Network Architecture Basics - Model Training and Evaluation Metrics - Testing Deep Learning Models |
| Testing AI-Specific Quality Characteristics | 20% | - Robustness Testing - Accuracy, Precision, Recall, F1-Score - Explainability and Interpretability - Fairness and Bias Detection |
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NEW QUESTION # 45
Which of the following is an example of overfitting?
Answer: C
Explanation:
Overfitting occurs when a machine learning (ML) model learns patterns that are too specific to the training data, leading to a lack of generalization for new, unseen data. This means the model performs exceptionally well on the training data but poorly on validation or test data because it has memorized the noise and minor details rather than learning the underlying patterns.
NEW QUESTION # 46
Which ONE of the following options for a test basis would give the LEAST coverage when using AI-based test generation?
Answer: A
Explanation:
An XML schema would give the least coverage when using AI-based test generation. While it defines the structure of data (e.g., tags, elements), it does not provide detailed information about the application functionality or behavior, which are essential for generating meaningful tests for AI systems. The other options (test model, web pages list, and pseudo-oracle) provide more comprehensive insights into the system's behavior and logic, which are better suited for effective AI-based test generation.
NEW QUESTION # 47
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION
Answer: B
Explanation:
In a neural network, the activation value of a neuron is determined by a combination of inputs from the previous layer, the weights of the connections, and the bias at the neuron level. Here's a detailed breakdown:
* Inputs for Activation Value:
* Activation Values of Neurons in the Previous Layer:These are the outputs from neurons in the preceding layer that serve as inputs to the current neuron.
* Weights Assigned to the Connections:Each connection between neurons has an associated weight, which determines the strength and direction of the input signal.
* Individual Bias at the Neuron Level:Each neuron has a bias value that adjusts the input sum, allowing the activation function to be shifted.
* Calculation:
* The activation value is computed by summing the weighted inputs from the previous layer and adding the bias.
* Formula: z=#(wi#ai)+bz = \sum (w_i \cdot a_i) + bz=#(wi#ai)+b, where wiw_iwi are the weights, aia_iai are the activation values from the previous layer, and bbb is the bias.
* The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation value.
* Why Option A is Correct:
* Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.
* Eliminating Other Options:
* B. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons: This option misses the bias, which is crucial.
* C. Individual bias at the neuron level, and weights assigned to the connections between the neurons: This option misses the activation values from the previous layer.
* D. Individual bias at the neuron level, and activation values of neurons in the previous layer: This option misses the weights, which are essential.
References:
* ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of neurons in a neural network.
* "Neural Network Activation Functions" (ISTQB CT-AI Syllabus, Section 6.1.1).
NEW QUESTION # 48
Which two test procedures are BEST suited for CleverPropose system testing?
Choose TWO options (2 out of 5)
Answer: A,E
Explanation:
The ISTQB CT-AI syllabus explains that AI-based decision-support systems benefit strongly fromback-to- back testingandmetamorphic testingwhen oracle problems exist or when limited regression tests are available.
In this scenario, CleverPropose replaces an older advisory system.Back-to-back testing(Option A) is ideal because the outputs of the existing conventional system can serve as areference, enabling comparison against the new AI system. This is exactly what the syllabus recommends when AI is replacing a traditional deterministic system.
Metamorphic testing(Option C) is also appropriate, as stated in Section4.6 - Metamorphic Relations. With limited regression tests and complex decision logic, testers can define metamorphic relations such as "if customer income increases, risk rating should not worsen." These relations allow validation even when exact expected outputs are unavailable.
Exploratory data analysis (Option D) is not a system testing technique. Pairwise testing (Option E) is not well suited for complex AI-based financial advice systems. Adversarial testing (Option B) is more relevant for security-critical or robustness evaluation, not primary system testing for advisory tools.
Thus,A and Care the correct and syllabus-supported choices.
NEW QUESTION # 49
Which of the following descriptions of quality aspects of a data set is correct?
Answer: D
Explanation:
The ISTQB CT-AI syllabus describes severaldata quality aspectsthat affect ML performance. In Section2.2 - Data Preparation, it explains that datasets may suffer from issues such as incomplete data, irrelevant data,incorrect data, unbalanced data, or data lacking preprocessing.
"Incomplete data" means thatportions of the required data are missing, often because some time periods, records, or sources were not captured. This aligns exactly with Option A, which correctly identifies missing intervals as incomplete data.
NEW QUESTION # 50
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