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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	systems from those required for conventional systems.
Topic 3	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4	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 5	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 6	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q36-Q41):

NEW QUESTION # 36
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it falls and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known health engines and ones which experienced a catastrophic fails due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.
What is the precision of this predictive model

A. 98.9%
B. 90.0%
C. 94.5%
D. 94.2%

Answer: D

Explanation:
Precision is a performance metric used to evaluate the accuracy of positive predictions in a classification model. It is defined by the formula:
Precision=TPTP+FP×100%\text{Precision} = \frac{TP}{TP + FP} \times 100\%Precision=TP+FPTP×100% Where:
* TP (True Positives)= Number of correctly predicted positive cases
* FP (False Positives)= Number of incorrectly predicted positive cases
The confusion matrix provided in the question would typically list these values. Based on ISTQB's guidelines for calculating precision, selecting the correct number of true positives and false positives from the given data should yield94.2%as the precision.
* Section 5.1 - Confusion Matrix and ML Functional Performance Metricsexplains the calculation of precisionusing the confusion matrix.
Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 37
Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?

A. Using a pixel comparison of the GUI before and after the change to check the differences.
B. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
C. Using a computer vision to compare the GUI before and after the test object changes.
D. Using a vision-based detection of the GUI layout changes before and after test object changes.

Answer: A

Explanation:
Pixel comparison is a traditional method and does not involve AI . It compares images at the pixel level, which can be effective but is not an intelligent approach. It is not considered an AI usage and is the least likely usage of AI for detecting GUI changes.

NEW QUESTION # 38
Which ONE of the below is MOST likely to indicate a problem with underfitting in an ML model?

A. The model uses a large amount of resources to make a prediction
B. The model fails to generalize on new data
C. The model is inaccurate on data similar to the training data
D. The model is vulnerable to adversarial attacks

Answer: C

Explanation:
The correct answer is D . Underfitting occurs when a model is not sophisticated enough to capture the patterns in the training data. The CT-AI syllabus states that underfitting models tend to be too simplistic and may struggle to provide accurate predictions both for new data and for data very similar to the training data.
Therefore, poor performance on data similar to the training data is the strongest indicator of underfitting.
Option B is more strongly associated with overfitting when considered alone. Overfitting occurs when the model fits the training data too closely and fails to generalize properly to new data. A model that performs well on training-like data but poorly on independent new data is therefore more likely overfitted than underfitted. Option A concerns adversarial robustness, not model fit. Option C concerns performance efficiency or resource consumption, not whether the model has learned the underlying data patterns. For exam purposes, the decisive diagnostic is whether the model is also inaccurate on training-like data; if so, underfitting is the best explanation.
References/topics: CT-AI Syllabus Chapter 3, Sections 3.5.1 "Overfitting" and 3.5.2 "Underfitting."
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NEW QUESTION # 39
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. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
B. Individual bias at the neuron level, 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 weights assigned to the connections between the neurons.
D. Individual bias at the neuron level, and activation values of neurons in the previous layer.

Answer: B

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 # 40
Which of the following is one of the reasons for data mislabelling?

A. Expert knowledge
B. Lack of domain knowledge
C. Interoperability error
D. Small datasets

Answer: B

Explanation:
The syllabus lists multiple reasons for mislabelled data, including the lack of domain knowledge:
"Lack of required domain knowledge may lead to incorrect labelling."

NEW QUESTION # 41
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