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ISTQB CT-AI Exam Syllabus Topics:

Section	Weight	Objectives
Testing AI-Specific Quality Characteristics	20%	- Explainability and Interpretability - Fairness and Bias Detection - Robustness Testing - Accuracy, Precision, Recall, F1-Score
Practical Considerations	12%	- Test Data Quality and Preparation - Documentation and Reporting - Automation in AI Testing - Test Environment Setup for AI
Neural Networks and Deep Learning	20%	- Neural Network Architecture Basics - Testing Deep Learning Models - Overfitting and Underfitting - Model Training and Evaluation Metrics
Basics of AI	8%	- Definitions and Terminology - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Data Concepts (Training, Validation, Test Data) - AI Types and Techniques
AI Test Methods and Techniques	20%	- Black-Box Testing for AI Systems - Adversarial Testing - Metamorphic Testing - Test Oracle Techniques for AI
Testing AI-Based Systems	20%	- Challenges in Testing AI-Based Systems - Testing AI-Specific Quality Characteristics - Test Levels for AI-Based Systems - Bias and Fairness Considerations

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q133-Q138):

NEW QUESTION # 133
A startup company has implemented a new facial recognition system for a banking application for mobile devices. The application is intended to learn at run-time on the device to determine if the user should be granted access. It also sends feedback over the Internet to the application developers. The application deployment resulted in continuous restarts of the mobile devices.
Which of the following is the most likely cause of the failure?

A. The size of the application is consuming too much of the phone's storage capacity
B. The feedback requires a physical connection and cannot be sent over the Internet
C. Mobile operating systems cannot process machine learning algorithms
D. The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle

Answer: D

Explanation:
The syllabus highlights that on-device training and processing require considerable computational power, which may exceed the capabilities of some mobile devices:
"Self-learning and continuous learning systems require large amounts of computational power, which can impact system performance and stability if the hardware is not powerful enough." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.3, page 22 of 99)

NEW QUESTION # 134
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection.
This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border.
Which associated risk is most likely to occur when using this pre-trained model?

A. Inherited bias: the model could have inherited unknown defects
B. There is no risk, as the model has already been trained
C. Improper data preparation
D. Insufficient function: the model was not trained to check for colors or words

Answer: A

Explanation:
According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case. Specifically, the syllabus states:
"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process." (Reference: ISTQB CT-AI Syllabus v1.0, Section 1.8.3)

NEW QUESTION # 135
Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?

A. Using a vision-based detection of the GUI layout changes before and after test object changes.
B. Using a computer vision to compare the GUI before and after the test object changes.
C. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
D. Using a pixel comparison of the GUI before and after the change to check the differences.

Answer: D

Explanation:
Pixel comparison is a traditional method and does not involve AI . It compares images at the pixel level, which can be effective but is not an intelligent approach. It is not considered an AI usage and is the least likely usage of AI for detecting GUI changes.

NEW QUESTION # 136
Which statement about the property of the test environment for an AI-based system is correct?

A. The test environment for an AI-based multi-agent system needs to act deterministically.
B. The test environment for a self-learning AI system needs to adapt to and learn from the test object.
C. The test environment for an autonomous AI system needs to perform both test design and execution autonomously.
D. The test environment for an AI system may need to include tools that can explain the decisions of the test object.

Answer: D

Explanation:
The ISTQB CT-AI syllabus (Section4.3 - Test Environments for AI Systems) describes that, unlike conventional software testing, testing AI systems may require specialized toolsfor analyzing and explaining the decisions of ML models. This includes visualization tools, explainability frameworks, and diagnostic utilities to understand why the AI made a certain prediction. Since AI decisions may be non-transparent, the test environment must supportexplainability, making Option B correct.

NEW QUESTION # 137
Which data-labeling approach uses a two-step process where labeling is first done by a tool and then verified or completed by a human?

A. Outsourced data labeling
B. Crowdsourced data labeling
C. Internal data labeling
D. AI-assisted data labeling

Answer: D

Explanation:
Section2.4 - Data Labeling Approachesof the ISTQB CT-AI syllabus explicitly definesAI-assisted data labelingas a hybrid process in which an automated tool performs the initial labeling and human annotators subsequently verify, correct, or complete the labels. This two-step process improves efficiency while retaining human oversight to ensure data quality. The syllabus describes this method as an effective compromise when manual labeling alone would be too slow or costly, and when initial automation can identify obvious patterns before a human provides the final authoritative labels.

NEW QUESTION # 138
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