Pass Guaranteed 2025 ISTQB CT-AI–Accurate Test Study Guide

Blog Article

Tags: CT-AI Test Study Guide, New CT-AI Test Pass4sure, Excellect CT-AI Pass Rate, New CT-AI Test Dumps, CT-AI Exam Questions Fee

The most notable feature of our CT-AI learning quiz is that they provide you with the most practical solutions to help you learn the exam points of effortlessly and easily, then mastering the core information of the certification course outline. Their quality of our CT-AI Study Guide is much higher than the quality of any other materials, and questions and answers of CT-AI training materials contain information from the best available sources.

ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 2	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 3	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 4	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 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	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 7	systems from those required for conventional systems.
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	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 11	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.

>> CT-AI Test Study Guide <<

CT-AI Study Materials & CT-AI VCE Dumps & CT-AI Test Prep

Under the leadership of a professional team, we have created the most efficient learning CT-AI training guide for our users. Our users use their achievements to prove that we can get the most practical knowledge in the shortest time. CT-AI exam questions are tested by many users and you can rest assured. If you want to spend the least time to achieve your goals, CT-AI Learning Materials are definitely your best choice. You can really try it we will never let you down!

ISTQB Certified Tester AI Testing Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates. How could AI help make the automation more reliable?

A. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly
B. It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation
C. It could modify the automation code to ignore unrecognizable objects to avoid failures
D. It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object

Answer: D

Explanation:
The syllabus discusses using AI-based tools to reduce GUI test brittleness:
"AI can be used to reduce the brittleness of this approach, by employing AI-based tools to identify the correct objects using various criteria (e.g., XPath, label, id, class, X/Y coordinates), and to choose the historically most stable identification criteria." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.6.1)

NEW QUESTION # 50
Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?
SELECT ONE OPTION

A. Test for human handover to give rest to the system.
B. Test for human handover after a given time interval.
C. Test for human handover requiring mandatory relinquishing control.
D. Test for human handover when it should actually not be relinquishing control.

Answer: D

Explanation:
* Testing Autonomy: Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems. The other tests are relevant to ensuring smooth operation and transitions between human and AI control.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Testing Autonomous AI-Based Systems and Testing for Human-AI Interaction.

NEW QUESTION # 51
Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?

A. It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution.
B. It has been trained to analyze mathematical models and is given a set of landscape pictures to classify.
C. It has been trained to analyze customer buying trend data and is given information on supplier cost data.
D. It has been trained to recognize cats and is given an image of a dog.

Answer: A

Explanation:
AI systems, particularly machine learning models, need to exhibit adaptability and flexibility to handle slight variations in input data without requiring retraining. The ISTQB CT-AI syllabus outlines adaptability as a crucial feature of AI systems, especially when the system is exposed to variations in its operational environment.
* Option A:"It has been trained to recognize cats and is given an image of a dog."
* This scenario introduces an entirely new class (dogs), which is outside the AI system's expected scope. If the AI was only trained to recognize cats, it would not be expected to recognize dogs correctly without retraining. This does not demonstrate adaptability as expected from an AI system.
* Option B:"It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution."
* This is an example of an AI system encountering a variation of its training data rather than entirely new data. Most AI-based image processing models can adapt to different resolutions by applying downsampling or other pre-processing techniques. Since the data remains within the domain of human faces, the model should be able to process the higher-resolution image without significant issues.
* Option C:"It has been trained to analyze mathematical models and is given a set of landscape pictures to classify."
* This represents a complete shift in the data type from structured numerical data to unstructured image data. The AI system is unlikely to adapt effectively, as it has not been trained on image classification tasks.
* Option D:"It has been trained to analyze customer buying trend data and is given information on supplier cost data."
* This introduces a significant domain shift. Customer buying trends focus on consumer behavior, while supplier cost data relates to pricing structures and logistics. The AI system would likely require retraining to process the new data meaningfully.
* Adaptability Requirements:The syllabus discusses that AI-based systems must be able to adapt to changes in their operational environment and constraints, including minor variations in input quality (such as resolution changes).
* Autonomous Learning & Evolution:AI systems are expected to improve and handle evolving inputs based on prior experience.
* Challenges in Testing Self-Learning Systems:AI systems should be tested to ensure they function correctly when encountering new but related data, such as different resolutions of the same object.
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,option Bis the best choice as it aligns with the adaptability characteristics expected from AI-based systems.

NEW QUESTION # 52
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?

A. Associating shoppers with their shopping tendencies
B. Estimating the expected purchase of cat food after a particularly successful ad campaign
C. Grouping individual fish together based on their types of fins
D. Classifying muffin purchases based on the perceived attractiveness of their packaging

Answer: A

Explanation:
The syllabus defines clustering as:
"Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.2, page 26 of 99)

NEW QUESTION # 53
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it fails and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known healthy engines and ones which experienced a catastrophic failure 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. 90.0%
B. 94.2%
C. 94.5%
D. 98.9%

Answer: C

Explanation:
The syllabus defines precision as:
"Precision = TP / (TP + FP) * 100%. Precision measures the proportion of positives that were correctly predicted." Using the confusion matrix:
* TP = 90
* FP = 10Thus: Precision = (90 / (90 + 10)) * 100% = 90 / 100 * 100% = 90%However, the confusion matrix totals suggest that the calculation should be done in the form:Precision = 90 / (90 + 10) * 100%
= 90%Since the given answers do not include exactly 90%, the closest approximation and the correct answer, as described in the syllabus, would be 90%.(Reference: ISTQB CT-AI Syllabus v1.0, Section
5.1, page 40 of 99)

NEW QUESTION # 54
......

Pass4SureQuiz CT-AI Questions have helped thousands of candidates to achieve their professional dreams. Our Certified Tester AI Testing Exam (CT-AI) exam dumps are useful for preparation and a complete source of knowledge. If you are a full-time job holder and facing problems finding time to prepare for the ISTQB CT-AI Exam Questions, you shouldn't worry more about it.

New CT-AI Test Pass4sure: https://www.pass4surequiz.com/CT-AI-exam-quiz.html

Report this page

PASS GUARANTEED 2025 ISTQB CT-AI–ACCURATE TEST STUDY GUIDE

Pass Guaranteed 2025 ISTQB CT-AI–Accurate Test Study Guide