2026 Perfect CT-AI–100% Free Certification Test Questions | Sample CT-AI Exam

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 4	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 5	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.
Topic 6	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 7	systems from those required for conventional systems.
Topic 8	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 9	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 10	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

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Sample ISTQB CT-AI Exam, Study CT-AI Dumps

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q125-Q130):

NEW QUESTION # 125
In which ONE of the following situations would an ML model be MOST effective at determining the criticality of new defects?

A. An old application where defect records are linked to failed tests and production incidents
B. An old application with few critical defect records and many non-critical defect records
C. An old application with lots of defect records but a brand new development and test team
D. A new application which is in the early stages of the first test cycle

Answer: A

Explanation:
An old application where defect records are linked to failed tests and production incidents would provide the most valuable data for an ML model to determine the criticality of new defects. By using historical data of defects that are linked to actual issues in production or testing failures, the model can learn patterns and correlations between defects and their criticality, making it highly effective in predicting the criticality of new defects. This type of historical data provides the necessary context for accurate predictions.

NEW QUESTION # 126
You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?

A. Transparency
B. Accessibility
C. Explorability
D. Autonomy

Answer: A

Explanation:
AI-based systems can sometimes behave like black boxes, where the internal decision-making process is unclear. Transparency refers to the ability to inspect and understand the training data, algorithms, and decision-making process of the AI system.
Transparency ensures that testers and stakeholders can review how an AI system was trained.
Access to training data is a key factor in transparency because it allows testers to analyze biases, completeness, and representativeness of the dataset.
Transparency is an essential characteristic of explainable AI (XAI). Having access to training data means that testers can investigate how data influences AI behavior.
Regulatory and ethical AI guidelines emphasize transparency. Many AI ethics frameworks, such as GDPR and Trustworthy AI guidelines, recommend transparency to ensure fair and explainable AI decision-making.

NEW QUESTION # 127
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. The input layer of a deep neural network must have at least as many neurons as its output layer
C. A single-layer perceptron is NOT a neural network
D. Training a neural network only changes the values of the weights at the connections between neurons

Answer: D

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 # 128
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

A. Regression
B. Clustering
C. Classification
D. Reinforcement learning

Answer: C

Explanation:
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:
Classification: This type of machine learning involves categorizing input data into predefined classes. In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
Why Not Other Options:
Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.
References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.

NEW QUESTION # 129
Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?

A. A system that utilizes human beings for all important decisions.
B. A fully automated manufacturing plant that uses no software.
C. A system that is fully able to respond to its environment.
D. A system that utilizes a tool like Selenium.

Answer: C

Explanation:
AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.

NEW QUESTION # 130
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