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Title: Test CT-AI Cram Pdf | CT-AI Practice Exams Free [Print This Page]
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Title: Test CT-AI Cram Pdf | CT-AI Practice Exams Free
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ISTQB CT-AI Exam Syllabus Topics:
TopicDetails
Topic 1
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 2
  • 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 3
  • systems from those required for conventional systems.
Topic 4
  • 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 5
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 6
  • 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 7
  • 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q105-Q110):NEW QUESTION # 105
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION
Answer: B
Explanation:
* Challenges for Acquiring Test Data in ML Systems: Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Preparation and Data Quality Issues.

NEW QUESTION # 106
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?
Answer: D
Explanation:
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.2, Page 67 of 99)

NEW QUESTION # 107
Which of the following is an example of overfitting?
Answer: C
Explanation:
Overfitting occurs when a machine learning (ML) model learns patterns that are too specific to the training data, leading to a lack of generalization for new, unseen data. This means the model performs exceptionally well on the training data but poorly on validation or test data because it has memorized the noise and minor details rather than learning the underlying patterns.
* Option A:"The model is not able to generalize to accommodate new types of data."
* This is the correct definition of overfitting. When a model cannot generalize beyond its training data, it struggles with new input, which results in overfitting.
* Option B:"The model is too simplistic for the data."
* This describes underfitting rather than overfitting. Underfitting happens when a model is too simple to capture the underlying patterns in the data.
* Option C:"The model is missing relationships between the inputs and outputs."
* This also aligns more with underfitting, where the model fails to capture important relationships in the data.
* Option D:"The model discards data it considers to be noise or outliers."
* While some ML models may ignore outliers, overfitting actually occurs when the model includes noise and outliers in its learning process rather than discarding them.
* Overfitting Definition:"Overfitting occurs when the model fits too closely to a set of data points and fails to properly generalize. It works well on training data but struggles with new data.".
* Testing for Overfitting:"Overfitting may be detected by testing the model with a dataset that is completely independent of the training dataset" Analysis of the Answer Options:ISTQB CT-AI Syllabus References:

NEW QUESTION # 108
Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
SELECT ONE OPTION
Answer: A
Explanation:
* Coverage for Neuron Activation Values: Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 6.2 Coverage Measures for Neural Networks, which details different types of coverage measures, including sign change coverage.

NEW QUESTION # 109
Which of the following statements regarding experience-based testing for AI-based systems is correct?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
The ISTQB CT-AI syllabus explains inSection 4.4 - Experience-Based Testing for AI Systemsthat AI- based systems frequently suffer frominsufficient specifications, unpredictable model behavior, andtest oracle problems, especially when outputs depend on probabilistic or learned patterns. The syllabus explicitly states thatexploratory testingis especially valuable in such contexts because it allows testers to investigate the system interactively, observe unexpected behavior, and evaluate system responses that cannot be fully predicted beforehand. Thus, OptionCaccurately reflects the role and justification of exploratory testing for AI systems.
Option A describes data analysis rather than intuitive test design. Option B is incorrect because checklist- based testing does not dynamically adapt test cases; instead, it follows predetermined checklists. Option D incorrectly defines "tour-based testing"; tours refer to structured exploratory approaches, not biased datasets.
Therefore,Option Cis the syllabus-aligned correct statement.

NEW QUESTION # 110
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