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[Hardware] Customizable CT-AI Practice Test Software (Desktop & Web-Based)

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【Hardware】 Customizable CT-AI Practice Test Software (Desktop & Web-Based)

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ISTQB CT-AI Exam Syllabus Topics:
TopicDetails
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
  • 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
  • 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 4
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 5
  • systems from those required for conventional systems.
Topic 6
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 7
  • 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 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
  • 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 10
  • 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.

Exam CT-AI PDF, New CT-AI Exam TopicsSuccess in the CT-AI certification exam is essential to advance your career. The Certified Tester AI Testing Exam (CT-AI) certification can set you apart from the competition and give you the edge you need to grow in your career. However, preparing for the CT-AI test can be challenging, mainly if you have limited time. Here's where DumpStillValid comes in with actual CT-AI Questions. We at DumpStillValid are well aware of the importance of the ISTQB CT-AI certification in order to stand out in today's competitive job environment.
ISTQB Certified Tester AI Testing Exam Sample Questions (Q102-Q107):NEW QUESTION # 102
When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?
  • A. Test cases to verify that the system automatically suppresses invalid output data
  • B. Test cases to detect the system appropriately automating its data input
  • 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." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.2, page 59 of 99)

NEW QUESTION # 103
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION
  • A. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
  • B. A comparison of the performance of two different ML implementations on the same input data.
  • C. A comparison of two different websites for the same company to observe from a user acceptance perspective.
  • D. A comparison of the performance of an ML system on two different input datasets.
Answer: D
Explanation:
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
* Understanding A/B Testing:
* In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
* Application in Machine Learning:
* In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
* Why Option C is the Least Descriptive:
* Option C describes comparing the performance of an ML system on two different input datasets.
This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
* Clarifying the Other Options:
* A. A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
* B. A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
* D. A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
References:
* ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
* "Understanding A/B Testing" (ISTQB CT-AI Syllabus).

NEW QUESTION # 104
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION
  • A. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
  • B. A comparison of the performance of two different ML implementations on the same input data.
  • C. A comparison of two different websites for the same company to observe from a user acceptance perspective.
  • D. A comparison of the performance of an ML system on two different input datasets.
Answer: D
Explanation:
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
Understanding A/B Testing:
In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
Application in Machine Learning:
In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
Why Option C is the Least Descriptive:
Option C describes comparing the performance of an ML system on two different input datasets. This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
Clarifying the Other Options:
A . A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
B . A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
D . A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
Reference:
ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
"Understanding A/B Testing" (ISTQB CT-AI Syllabus).

NEW QUESTION # 105
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. Autonomy
  • B. Transparency
  • C. Accessibility
  • 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 # 106
Which of the following technologies for implementing AI is considered to be a reasoning technique?
Choose ONE option (1 out of 4)
  • A. Random Forest
  • B. Deductive classifiers
  • C. Linear regression
  • D. Genetic algorithms
Answer: B
Explanation:
TheISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is"Reasoning techniques."These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning. Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.
Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.
The other options-Linear regression,Random Forest, andGenetic algorithms-are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.
Thus, based on the authoritative definitions in the syllabus,Deductive classifiers (Option A)is the only technology classified as a reasoning technique.

NEW QUESTION # 107
......
Certified Tester AI Testing Exam (CT-AI) Practice exams (desktop and web-based) are designed solely to help you get your Certified Tester AI Testing Exam (CT-AI) certification on your first try. Our ISTQB CT-AI mock test will help you understand the Certified Tester AI Testing Exam (CT-AI) exam inside out and you will get better marks overall. It is only because you have practical experience of the Certified Tester AI Testing Exam (CT-AI) exam even before the exam itself.
Exam CT-AI PDF: https://www.dumpstillvalid.com/CT-AI-prep4sure-review.html
P.S. Free 2026 ISTQB CT-AI dumps are available on Google Drive shared by DumpStillValid: https://drive.google.com/open?id=1pBJXBC5Q6Ctskp3Q1ral-sPZtwkzXqrO
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