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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 2	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 3	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 4	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 5	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 6	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 7	systems from those required for conventional systems.
Topic 8	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q76-Q81):

NEW QUESTION # 76
When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?

A. Test cases to detect the system appropriately automating its data input
B. Test cases to detect the system prompting for unnecessary human intervention
C. Test cases to verify that the system automatically confirms the correct classification of training data
D. Test cases to verify that the system automatically suppresses invalid output data

Answer: B

Explanation:
When verifyingautonomous AI-based systems, a critical aspect is ensuring that they maintain an appropriate level of autonomy whileonly requesting human intervention when necessary. If an AI system unnecessarily asks for human input, it defeats the purpose of autonomy and can:
* Slow down operations.
* Reduce trust in the system.
* Indicate improper confidence thresholds in decision-making.
This is particularly crucial inautonomous vehicles, AI-driven financial trading, and robotic process automation, where excessive human intervention would hinder performance.
* A. Test cases to verify that the system automatically confirms the correct classification of training data# This is relevant for verifying training consistency but not for autonomy validation.
* B. Test cases to detect the system appropriately automating its data input# While relevant, data automation does not directly address the verification of autonomy.
* D. Test cases to verify that the system automatically suppresses invalid output data# This focuses on output filtering rather than decision-making autonomy.
Why are the other options incorrect?Thus, the mostcritical test casefor verifyingautonomous AI-based systemsis ensuring that itdoes not unnecessarily request human intervention.
* Section 8.2 - Testing Autonomous AI-Based Systemsstates that it is crucial to testwhether the system requests human intervention only when necessaryand does not disrupt autonomy.
Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 77
Which ONE of the following hardware is MOST suitable for implementing Al when using ML?
SELECT ONE OPTION

A. Hardware supporting high precision floating point operations.
B. High powered CPUs.
C. 64-bit CPUs.
D. Hardware supporting fast matrix multiplication.

Answer: D

Explanation:
A . 64-bit CPUs.
While 64-bit CPUs are essential for handling large amounts of memory and performing complex computations, they are not specifically optimized for the types of operations commonly used in machine learning.
B . Hardware supporting fast matrix multiplication.
Matrix multiplication is a fundamental operation in many machine learning algorithms, especially in neural networks and deep learning. Hardware optimized for fast matrix multiplication, such as GPUs (Graphics Processing Units), is most suitable for implementing AI and ML because it can handle the parallel processing required for these operations efficiently.
C . High powered CPUs.
High powered CPUs are beneficial for general-purpose computing tasks and some aspects of ML, but they are not as efficient as specialized hardware like GPUs for matrix multiplication and other ML-specific tasks.
D . Hardware supporting high precision floating point operations.
High precision floating point operations are important for scientific computing and some specific AI tasks, but for many ML applications, fast matrix multiplication is more critical than high precision alone.
Therefore, the correct answer is B because hardware supporting fast matrix multiplication, such as GPUs, is most suitable for the parallel processing requirements of machine learning.

NEW QUESTION # 78
Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?
SELECT ONE OPTION

A. Using a computer vision to compare the GUI before and after the test object changes.
B. Using a vision-based detection of the GUI layout changes before and after 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:
* A. Using a pixel comparison of the GUI before and after the change to check the differences.
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.
* B. Using computer vision to compare the GUI before and after the test object changes.
Computer vision involves using AI techniques to interpret and process images. It is a likely usage of AI for detecting changes in the GUI .
* C. Using vision-based detection of the GUI layout changes before and after test object changes.
Vision-based detection is another AI technique where the layout and structure of the GUI are analyzed to detect changes. This is a typical application of AI .
* D. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
An ML-based classifier can intelligently determine significant changes and decide if they need human review, which is a sophisticated AI application.

NEW QUESTION # 79
In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.
SELECT ONE OPTION

A. Testing for accuracy
B. Testing for security
C. Testing for bias
D. Testing for concept drift

Answer: D

Explanation:
* Type of Testing for Stock-Price Prediction Models: Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive losses in coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors.

NEW QUESTION # 80
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION

A. GUI analysis by computer vision
B. Natural language processing on textual requirements
C. Machine learning on logs of execution
D. Analyzing source code for generating test cases

Answer: B

Explanation:
When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:
Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.
Why Not Other Options:
Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.
Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.
GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.

NEW QUESTION # 81
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