Free Sample ISTQB CT-AI Questions - New CT-AI Exam Practice

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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester AI Testing (CT-AI) Exam
Exam Number:	CT-AI
Exam Duration:	60 minutes
Passing Score:	65%
Exam Format:	Multiple Choice
Related Certifications:	ISTQB Certified Tester Foundation Level (CTFL)
Available Languages:	English
Certificate Validity Period:	Lifetime
Real Exam Qty:	40
Recommended Training:	ISTQB Accredited Training Providers
Exam Registration:	ISTQB Official Website
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online and onsite proctored exam via accredited ISTQB examination providers
Pre Condition:	Recommended prior knowledge of ISTQB Foundation Level (CTFL) and basic understanding of software testing concepts and machine learning fundamentals.
Official Syllabus URL:	https://www.istqb.org

>> Free Sample ISTQB CT-AI Questions <<

ISTQB - CT-AI - Certified Tester AI Testing Exam –Professional Free Sample Questions

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 3	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 4	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 5	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 6	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 7	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 8	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 9	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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q48-Q53):

NEW QUESTION # 48
There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?

A. Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers
B. Use it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes
C. Use it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions
D. Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas

Answer: A

Explanation:
The syllabus explains that ML models can be used to analyze reported defects and suggest which developers are best suited to fix them based on historical data about defect assignment and resolution speed:
"Assignment: ML models can suggest which developers are best suited to fix particular defects, based on the defect content and previous developer assignments." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.2, page 78 of 99)

NEW QUESTION # 49
Which ONE of the below is MOST likely to be a challenge in ML data preparation?

A. The time and associated costs required
B. Functional performance
C. Designing a reward function
D. Specification of acceptance criteria

Answer: A

Explanation:
In machine learning, data preparation is often the most time-consuming and costly phase. It involves collecting, cleaning, and transforming raw data into a usable form for training models.
This can require significant effort and resources, making it the most likely challenge compared to the other options listed.

NEW QUESTION # 50
Which ONE of the following options is a technology used to implement AI?

A. Autonomy
B. Classification
C. Genetic algorithms
D. Reinforcement learning

Answer: C

Explanation:
Genetic algorithms are a technology used in AI, particularly in optimization problems and machine learning models. They are inspired by the process of natural selection and evolve solutions over generations.

NEW QUESTION # 51
Which of the following is an example of overfitting?

A. The model is not able to generalize to accommodate new types of data
B. The model is too simplistic for the data
C. The model discards data it considers to be noise or outliers
D. The model is missing relationships between the inputs and outputs

Answer: A

Explanation:
The syllabus defines overfitting as:
"Overfitting is when the ML model learns the training data so well that it is unable to generalize to accommodate new data." This occurs when the model memorizes the training data, including noise, instead of learning the general patterns.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.5.1, page 31 of 99)

NEW QUESTION # 52
Which ONE of the below is NOT an input to an activation function in a neural network?

A. Bias
B. Activation values from the previous layer
C. The weights assigned to the connections
D. Activation values form the current layer

Answer: D

Explanation:
The correct answer is C . In a neural network, each neuron calculates its activation value using an activation function. The CT-AI syllabus explains that the activation function receives as input the activation values from all neurons in the previous layer , the weights assigned to the connections between neurons, and the individual bias of the neuron.
Therefore, options A, B, and D are valid inputs to the activation function. Bias is a preset constant associated with the neuron. The activation values from the previous layer represent the incoming signal. The weights assigned to the connections determine how strongly those incoming activation values influence the neuron's calculation. Option C is not an input because activation values from the current layer are the outputs of activation functions being computed in that layer, not inputs to the same computation. In feed-forward neural network logic, information passes from the input layer through hidden layers toward the output layer; each layer computes its activation values based on the previous layer, not on activation values simultaneously generated in the current layer.
References/topics: CT-AI Syllabus Chapter 6, Section 6.1 "Neural Networks"; Appendix B "Activation function" and "Activation value."
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NEW QUESTION # 53
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