ISTQB CT-AI Frequent Updates & CT-AI Practice Test

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ISTQB CT-AI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Practical Considerations	12%	- Test Environment Setup for AI - Documentation and Reporting - Test Data Quality and Preparation - Automation in AI Testing
Topic 2: Testing AI-Specific Quality Characteristics	20%	- Explainability and Interpretability - Accuracy, Precision, Recall, F1-Score - Robustness Testing - Fairness and Bias Detection
Topic 3: Basics of AI	8%	- Data Concepts (Training, Validation, Test Data) - Definitions and Terminology - AI Types and Techniques - Machine Learning Types (Supervised, Unsupervised, Reinforcement)
Topic 4: AI Test Methods and Techniques	20%	- Adversarial Testing - Test Oracle Techniques for AI - Metamorphic Testing - Black-Box Testing for AI Systems
Topic 5: Testing AI-Based Systems	20%	- Test Levels for AI-Based Systems - Bias and Fairness Considerations - Testing AI-Specific Quality Characteristics - Challenges in Testing AI-Based Systems
Topic 6: Neural Networks and Deep Learning	20%	- Model Training and Evaluation Metrics - Neural Network Architecture Basics - Overfitting and Underfitting - Testing Deep Learning Models

>> ISTQB CT-AI Frequent Updates <<

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which of the following is a dataset issue that can be resolved using pre-processing?

A. Insufficient data
B. Wanted outliers
C. Invalid data
D. Numbers stored as strings

Answer: D

Explanation:
The syllabus describes that data pre-processing includes cleaning (e.g., fixing or removing invalid data) and transforming data (e.g., changing data types such as numbers stored as strings).
"Transformation: The format of the given data is changed... converting categorical data into numerical data, changing image formats..." (Reference: ISTQB CT-AI Syllabus v1.0, Section 4.1.1, Page 34 of 99)

NEW QUESTION # 61
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required, thus reducing the time needed for each testing cycle.
How can an AI-based tool be expected to assist in this reduction?

A. By using A/B testing to compare the last update with the newest change and compare metrics between the two
B. By performing Bayesian analysis to estimate the types of human interactions that are expected to be seen in the system and then selecting those test cases
C. By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases
D. By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category

Answer: C

Explanation:
The syllabus mentions that AI can help optimize regression test suites:
"An AI-based tool can perform optimization of the regression test suite by analyzing... the information from previous test results, associated defects, and the latest changes that have been made, such as features which are broken more frequently and which tests exercise code impacted by recent changes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.4, page 79 of 99)

NEW QUESTION # 62
A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options. When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market the only response from the automated system is: "I don't understand your question." This AI system should be categorized as?

A. Narrow AI
B. Super AI
C. Conventional AI
D. General AI

Answer: A

Explanation:
Narrow AI (also known as Weak AI) is designed to perform specific tasks or solve particular problems within a limited domain, such as booking a ticket or recognizing speech. In this case, the AI system is specialized in handling flight bookings and related tasks but is not capable of addressing broader or more complex questions, like weather conditions or labor market impacts.

NEW QUESTION # 63
Which of the following statements about the structure and function of neural networks is true?

A. The bias of a neuron is determined by the activation values of the neurons in the previous layer
B. A single-layer perceptron is NOT a neural network
C. Training a neural network only changes the values of the weights at the connections between neurons
D. The input layer of a deep neural network must have at least as many neurons as its output layer

Answer: C

Explanation:
Section1.7 - Neural Networksof the ISTQB CT-AI syllabus explains that neural networks consist of neurons connected by weighted links. During training,learning occurs by adjusting the weights on these connections. This is the essence of gradient descent and backpropagation. Option B correctly states this behavior: only the weights are modified, not the activation functions, neuron counts, or architectural structure.

NEW QUESTION # 64
Which of the following statements about explainable AI is correct?

A. Interpretability refers to how easily users can determine whether the result provided by the AI- based system is correct
B. According to The Royal Society, one reason for explainable AI is to increase user confidence in the system
C. Explainability refers to how easily the algorithms and training data needed to create the model can be determined
D. According to The Royal Society, one reason for explainable AI is to eliminate the need for risk and vulnerability assessments

Answer: B

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 # 65
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