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Pass Guaranteed ISTQB - CT-AI - Trustable Certified Tester AI Testing Exam Fresh Dumps

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ISTQB CT-AI Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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 5

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 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

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

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 9

Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

Topic 10

systems from those required for conventional systems.

Topic 11

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 (Q87-Q92):

NEW QUESTION # 87
A car insurance company is using a new AI service to reward defensive driving behavior among its policyholders. The driving behavior is recorded in a rating number (score).
The AI service determines this score from the following input values:
Reference speed v_max in km/h
Average speed v_mean in km/h
Average acceleration a_pos in m/s2
Average braking deceleration a_neg in m/s2
The more defensive the driving behavior is (slow driving, low acceleration, low braking deceleration), the higher is the score.
Three initial test cases (Test 1 to Test 3) are used for testing the AI service. In addition, new test cases A-D are proposed.

Which of the new tests is NOT a follow-up test case for metamorphic testing?

A. Test C is not a follow-up test case.
B. Test B is not a follow-up test case.
C. Test D is not a follow-up test case.
D. Test A is not a follow-up test case.

Answer: B

Explanation:
According to the ISTQB CT-AI syllabus,metamorphic testingworks by applyingmetamorphic relations (MRs): predictable input transformations that should lead to predictable output changes.
From the initial test data, clear relations emerge for defensive driving scoring. The score increases when:
v_mean decreases,
a_pos decreases,
a_neg becomes less negative,and decreases when the opposite occurs.
A valid metamorphic follow-up test must modify inputs in a direction consistent with at least one MR while keeping the expected output direction predictable.
Test Alowers v_mean compared to Test 1, with similar acceleration values. This directly satisfies the MR that lower speed -> higher score.
Test Cincreases both acceleration and braking intensity compared to Test 2, making the reduced score range (30-70) consistent with more aggressive driving.
Test Dmodifies acceleration and braking magnitudes in ways consistent with Test 3's defensive- driving scoring boundaries.
Test B, however, changes multiple variables in contradictory directions:
v_mean increases (worse)
a_pos increases (worse)
a_neg becomes less negative (better)
Because these changes conflict,the expected score trend becomes unpredictable, violating the premise of a metamorphic follow-up test.
Thus Test B cannot be considered a metamorphic follow-up, which makes Option C correct.

NEW QUESTION # 88
Which supervised-learning classification/regression statement is correct?
Choose ONE option (1 out of 4)

A. Recognizing a dog from many different images is a regression problem
B. Predicting that diesel prices will increase by ~10% is a classification problem
C. In classification, objects are always assigned to exactly two classes
D. Deciding whether an object is a bicycle or a motorcycle is a classification problem

Answer: D

Explanation:
The ISTQB CT-AI syllabus explains supervised learning under Section1.6 - Machine Learning Approaches
. It definesclassificationas predictingcategorical labels, whereasregressionpredictscontinuous numerical values. OptionB-deciding whether an object is a bicycle or a motorcycle-fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation .
Option A is incorrect because image recognition of a dog is aclassificationtask, not regression. Option C is incorrect because predicting a 10% price rise involves forecasting anumerical value, which is aregression problem. Option D is incorrect because classification can involveany number of classes, not only two.
Multiclass classification is explicitly mentioned in the syllabus.
Therefore, OptionBis the only answer aligned with the syllabus' definitions.

NEW QUESTION # 89
Which statement about testing to prevent data poisoning and adversarial attacks is correct?

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. The adversarial examples identified during adversarial testing must not be added to the training data so that they do not poison the model.
D. Regression testing can be used to verify data sourcing policies to ensure the source of training data.

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. Option C correctly reflects this syllabus definition: adversarial testing is aboutusing attacks to locate weaknesses so they can be removed or mitigated.

NEW QUESTION # 90
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?

A. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
B. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
C. Individual bias at the neuron level, and activation values of neurons in the previous layer.
D. Individual bias at the neuron level, and weights assigned to the connections between the neurons.

Answer: A

Explanation:
Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.

NEW QUESTION # 91
You are using a neural network to train a robot vacuum to navigate without bumping into objects. You set up a reward scheme that encourages speed but discourages hitting the bumper sensors. Instead of what you expected, the vacuum has now learned to drive backwards because there are no bumpers on the back.
This is an example of what type of behavior?

A. Transparency
B. Interpretability
C. Reward-hacking
D. Error-shortcircuiting

Answer: C

Explanation:
The syllabus defines reward hacking as:
"Reward hacking can result from an AI-based system achieving a specified goal by using a 'clever' or 'easy' solution that perverts the spirit of the designer's intent." In this case, the vacuum found a loophole in the reward function-driving backwards to avoid bumper triggers while maximizing reward for speed.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.6, page 24 of 99)

NEW QUESTION # 92
......

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