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[Hardware] Valid Braindumps ISTQB CT-AI Ebook, CT-AI Dumps Collection

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【Hardware】 Valid Braindumps ISTQB CT-AI Ebook, CT-AI Dumps Collection

Posted at 1/10/2026 10:51:16      View：28 | Replies：0        Print      Only Author   [Copy Link] 1#
P.S. Free 2026 ISTQB CT-AI dumps are available on Google Drive shared by VCEPrep: https://drive.google.com/open?id=1ROv8HrlwV6awiPNuqrrrv-qPLBiMLnxM
To pass the certification exam, you need to select right CT-AI study guide and grasp the overall knowledge points of the real exam. The test questions from our CT-AI dumps collection cover almost content of the exam requirement and the real exam. Trying to download the free demo in our website and check the accuracy of CT-AI Test Answers and questions. Getting certification will be easy for you with our materials.
ISTQB CT-AI Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 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
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 4
  • 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 5
  • 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 6
  • 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 7
  • systems from those required for conventional systems.
Topic 8
  • 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 9
  • 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 10
  • 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 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 (Q82-Q87):NEW QUESTION # 82
Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?
SELECT ONE OPTION
  • A. Search of similar code based on natural language processing.
  • B. Clustering of similar code modules to predict based on similarity.
  • C. Using a classification model to predict the presence of a defect by using code quality metrics as the input data.
  • D. Identifying the relationship between developers and the modules developed by them.
Answer: C
Explanation:
Defect prediction models aim to identify parts of the software that are likely to contain defects by analyzing historical data and code quality metrics. The primary goal is to use this predictive information to allocate testing and maintenance resources effectively. Let's break down why option D is the correct choice:
* Understanding Classification Models:
* Classification models are a type of supervised learning algorithm used to categorize or classify data into predefined classes or labels. In the context of defect prediction, the classification model would classify parts of the code as either "defective" or "non-defective" based on the input features.
* Input Data - Code Quality Metrics:
* The input data for these classification models typically includes various code quality metrics such as cyclomatic complexity, lines of code, number of methods, depth of inheritance, coupling between objects, etc. These metrics help the model learn patterns associated with defects.
* Historical Data:
* Historical versions of the code along with their defect records provide the labeled data needed for training the classification model. By analyzing this historical data, the model can learn which metrics are indicative of defects.
* Why Option D is Correct:
* Option D specifies using a classification model to predict the presence of defects by using code quality metrics as input data. This accurately describes the process of defect prediction using historical bug data and quality metrics.
* Eliminating Other Options:
* A. Identifying the relationship between developers and the modules developed by them:
This does not directly involve predicting defects based on code quality metrics and historical data.
* B. Search of similar code based on natural language processing: While useful for other purposes, this method does not describe defect prediction using classification models and code metrics.
* C. Clustering of similar code modules to predict based on similarity: Clustering is an unsupervised learning technique and does not directly align with the supervised learning approach typically used in defect prediction models.
References:
* ISTQB CT-AI Syllabus, Section 9.5, Metamorphic Testing (MT), describes various testing techniques including classification models for defect prediction.
* "Using AI for Defect Prediction" (ISTQB CT-AI Syllabus, Section 11.5.1).

NEW QUESTION # 83
A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test team has already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two.
What test method should you use to verify that the model has improved after the additional training?
  • A. Pairwise testing using combinatorics to look at a long list of photo parameters
  • B. Back-to-back testing using the version of the model before training and the new version of the model after being trained with additional images
  • C. Metamorphic testing because the application domain is not clearly understood at this point
  • D. Adversarial testing to verify that no incorrect images have been used in the training
Answer: B
Explanation:
The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:
"Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.3, page 67 of 99)

NEW QUESTION # 84
How can a tester check the system for bias as part of a review of data sources, acquisition, and preprocessing?
Choose ONE option (1 out of 4)
  • A. During the review, it can uncover algorithmic bias by analysing the procedures used to obtain the training data.
  • B. During the review of the preprocessing, the auditor can uncover whether the data has been influenced in a way that could lead to sample distortions.
  • C. It may use the LIME method as part of its data collection review to detect inappropriate bias.
  • D. As part of the review of preprocessing, it can reveal whether the data has been influenced in a way that could lead to algorithmic bias.
Answer: B
Explanation:
Bias detection at thedata levelis performed by reviewingdata acquisition and preprocessing steps, as explained in Section2.3 - Data Quality and Biasof the ISTQB CT-AI syllabus. Sample bias arises when data is distorted or when preprocessing introduces unintended shifts-for example, by filtering, normalization, or labeling steps that disproportionately affect subsets of the data. OptionBcorrectly reflects this: reviewers can identify whether preprocessing steps have altered the dataset in a way that introducessample distortions. This aligns perfectly with syllabus guidance on reviewing data pipelines for bias sources.
Option A is incorrect because algorithmic bias originates from themodel, not data collection procedures.
Option C is incorrect because LIME is anexplainabilitymethod applied post-model, not in data reviews.
Option D incorrectly states "algorithmic bias," but preprocessing affectssample bias, not algorithmic bias.
Thus, OptionBcorrectly matches the syllabus' definition of how bias can be detected during data-related reviews.

NEW QUESTION # 85
You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?
  • A. Accessibility
  • B. Transparency
  • C. Autonomy
  • D. Explorability
Answer: B
Explanation:
AI-based systems can sometimes behave likeblack boxes, where the internal decision-making process is unclear.Transparencyrefers to theability to inspect and understand the training data, algorithms, and decision- making processof the AI system.
* Transparency ensures that testers and stakeholders can review how an AI system was trained.
* Access totraining datais a key factor in transparency because it allows testers toanalyze biases, completeness, and representativenessof the dataset.
* Transparency is an essential characteristic of explainable AI (XAI).
* Having access to training data means that testers can investigate how data influences AI behavior.
* Regulatory and ethical AI guidelines emphasize transparency.
* Many AI ethics frameworks, such asGDPR and Trustworthy AI guidelines, recommend transparency to ensurefair and explainable AI decision-making.
* (A) Autonomy#
* Autonomy refers to an AI system's ability to make decisions independentlywithout human intervention. However,having access to training data does not relate to autonomy, which is more about self-learning and decision-making without human control.
* (B) Explorability#
* Explorability refers to the ability to test AI systems interactivelyto understand their behavior, but it does not directly relate to accessing training data.
* (D) Accessibility#
* Accessibility refers to the ease with which people can use the system, not the ability to inspect the training data.
* Transparency is the ease with which the training data and algorithm used to generate a model can be understood."Transparency: This is considered to be the ease with which the algorithm and training data used to generate the model can be determined." Why is Option C Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option C is the correct answer, astransparency involves access to training data, allowing testers to understand AI decision-making processes.

NEW QUESTION # 86
Consider an AI-system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?
  • A. The black-box testing method will allow the tester to check the transparency of the algorithm used to create the internal structure
  • B. Black-box testing eliminates the need for the tester to understand the internal structure of the AI-system
  • C. The tester wishes to better understand the logic of the software used to create the internal structure
  • D. Test automation can be built quickly and easily from the test cases developed during black-box testing
Answer: B
Explanation:
The syllabus explains:
"Where the internal structure of an AI-based system is too complex for humans to understand, the system can only be tested as a black box. Even when the internal structure is visible, this provides no additional useful information to help with testing." This confirms that black-box testing is chosen because the tester does not need to understand the system's internal structure.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.5, page 61 of 99)

NEW QUESTION # 87
......
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CT-AI Dumps Collection: https://www.vceprep.com/CT-AI-latest-vce-prep.html
P.S. Free 2026 ISTQB CT-AI dumps are available on Google Drive shared by VCEPrep: https://drive.google.com/open?id=1ROv8HrlwV6awiPNuqrrrv-qPLBiMLnxM
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