ISTQB CT-AI Certification Dumps & New CT-AI Test Pdf

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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	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	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 5	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 6	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 7	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 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	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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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ISTQB Certified Tester AI Testing Exam Sample Questions (Q131-Q136):

NEW QUESTION # 131
Which of the following is a problem with AI-generated test cases that are generated from the requirements?

A. They are slow and will usually not be able to execute in the time allowed.
B. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures.
C. They are defect prone because they are unable to detect nuances in the requirements.
D. They make debugging more complicated because the number of steps is usually high in order to induce the target failure.

Answer: B

Explanation:
AI-generated test cases are often created using machine learning (ML) models or heuristic algorithms. While these can be effective in generating large numbers of test cases quickly, they often suffer from the "test oracle problem." Test Oracle Problem: A test oracle is the mechanism used to determine the expected output of a test case. AI-generated test cases often lack expected results because AI-based tools do not inherently understand what the correct output should be.
Difficulty in Verification: Without expected results, verifying test cases becomes challenging.
Testers must rely on heuristics, anomaly detection, or significant failures, rather than traditional pass/fail conditions.

NEW QUESTION # 132
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 verify that the system automatically suppresses invalid output data
C. Test cases to detect the system prompting for unnecessary human intervention
D. Test cases to verify that the system automatically confirms the correct classification of training data

Answer: C

Explanation:
The syllabus highlights that testing for unnecessary human intervention is a key focus for autonomous AI-based systems:
"For autonomous AI-based systems, testers must ensure that the system does not prompt for unnecessary human intervention, as this contradicts the autonomy concept."

NEW QUESTION # 133
Which ONE of the below statements BEST describes why test environments for autonomous systems might need to be different to other test environments?

A. Tools may be required to simulate extreme scenarios
B. Tools may be required to provide explanations of system behaviour
C. AI-specific hardware may be required
D. Non determinism may need to be introduced into the environment

Answer: A

Explanation:
The correct answer is A . For autonomous systems, the distinguishing test-environment issue is that the system must respond to changes in its environment without human intervention and must also recognize situations where autonomy should be ceded back to human operators. The CT-AI syllabus states that, for some autonomous systems, identifying and mimicking the circumstances for ceding autonomy may require test environments to push the systems to extremes . It also notes that some autonomous systems operate in hazardous environments, making representative real-world testing difficult or unsafe.
Option B relates more specifically to multi-agent AI systems, where the environment may need non- determinism to mimic other interacting AI-based systems. Option C is a general AI-environment consideration where AI-specific processors may need inclusion. Option D is associated with explainability, where tools may be needed to understand decisions. These are valid AI test-environment factors, but they are not the best autonomy-specific reason. Virtual environments are particularly valuable because dangerous, unusual, and extreme scenarios can be tested safely and repeatedly.
References/topics: CT-AI Syllabus Chapter 10, Sections 10.1 and 10.2 "Test Environments for AI-Based Systems."
=========

NEW QUESTION # 134
Which of the following statements about the structure and function of neural networks is true?
Choose ONE option (1 out of 4)

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

Answer: B

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 theweightsare modified, not the activation functions, neuron counts, or architectural structure.
Option A is incorrect because a neuron'sbiasis not determined by previous activations; it is an independent trainable parameter added to the weighted input sum. Option C is incorrect because the syllabus states that a single-layer perceptron is a valid type of neural network, although limited to linearly separable problems.
Option D is incorrect because no rule requires the number of input neurons to exceed or equal the number of output neurons. Instead, input neurons correspond to thenumber of features, while output neurons correspond totasks or classes.
Therefore,Option Bprecisely reflects the syllabus definition of what changes during neural network training.

NEW QUESTION # 135
Which statement about testing to prevent data poisoning and adversarial attacks is correct?
Choose ONE option (1 out of 4)

A. Adversarial testing consists of using adversarial attacks to identify vulnerabilities so that they can be eliminated.
B. Using AIB testing to identify data poisoning can better identify outliers than exploratory data analysis.
C. Regression testing can be used to verify data sourcing policies to ensure the source of training data.
D. The adversarial examples identified during adversarial testing must not be added to the training data so that they do not poison the model.

Answer: A

Explanation:
The ISTQB CT-AI syllabus explains inSection 4.5 - Testing AI-Specific Risksthat adversarial testing is a structured test activity in which testers applyadversarial attacks-crafted or perturbed inputs-to intentionally expose weaknesses in the ML model. The purpose is to identify vulnerabilities that could be exploited throughdata poisoning,evasion attacks, orinput manipulation. OptionCcorrectly reflects this syllabus definition: adversarial testing is aboutusing attacks to locate weaknesses so they can be removed or mitigated.
Option A is incorrect because regression testing does not verify data sourcing policies; it verifies unchanged functionality after modifications. Option B is incorrect because adversarial examples are oftenaddedto training datasets to improverobustness(a practice called adversarial training), not excluded. Option D is incorrect because AIB testing is not described as superior to exploratory data analysis in outlier detection; both have different purposes, and EDA remains essential for data quality assessment.
Thus,Option Cis consistent with syllabus-defined adversarial testing.

NEW QUESTION # 136
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