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Title: Latest CT-AI Practice Materials, Test CT-AI Assessment [Print This Page]
Author: leodavi838    Time: yesterday 23:39
Title: Latest CT-AI Practice Materials, Test CT-AI Assessment
2026 Latest Actual4test CT-AI PDF Dumps and CT-AI Exam Engine Free Share: https://drive.google.com/open?id=1iuwewGPLpJ9b36oG_6X0uoeSEly0m2gx
Since our Certified Tester AI Testing Exam practice exam tracks your progress and reports results, you can review these results and strengthen your weaker concepts. We offer ISTQB CT-AI desktop practice test software which works on Windows computers after installation. The web-based CT-AI practice exam needs no plugins or software installation. Linux, iOS, Android, Windows, and Mac support the web-based ISTQB CT-AI Practice Exam. Additionally, Chrome, Opera, Firefox, Safari, Internet Explorer support this Certified Tester AI Testing Exam CT-AI web-based practice test.
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
  • 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 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
  • 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 5
  • systems from those required for conventional systems.
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
  • 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.

>> Latest CT-AI Practice Materials <<
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ISTQB Certified Tester AI Testing Exam Sample Questions (Q119-Q124):NEW QUESTION # 119
Which statement regarding testing transparency, explainability, or interpretability is MOST correct?
Choose ONE option (1 out of 4)
Answer: A
Explanation:
The ISTQB CT-AI syllabus states in Section2.10 - Explainability, Transparency, and Interpretabilitythat interpretability isuser-dependent, meaning different users understand explanations differently. This is because interpretability depends not only on the ML algorithm but also on the user's domain knowledge, experience, and expectations. OptionBdirectly reflects this syllabus principle: interpretability testing must consideruser background, and explanations must be comprehensible to the intended user group.
Option A is incorrect because explainability testing requires substantial information about the model, data, and expected behavior-not just exploratory effort. Option C is incorrect because explainability isnot generally quantifiable through dynamic testing alone, and the syllabus does not assert model-type specificity in this way. Option D exaggerates LIME's capabilities. LIME offers approximate local explanations, but cannotpreciselystate root causes; the syllabus emphasizes itslimitationsand that explanations are approximations, not exact reasons.
Therefore,Option Bis the most syllabus-aligned and correct statement.

NEW QUESTION # 120
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?
Answer: D
Explanation:
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1)

NEW QUESTION # 121
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION
Answer: C
Explanation:
* Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics.

NEW QUESTION # 122
Before deployment of an AI-based system, a developer is expected to demonstrate in a test environment how decisions are made. Which of the following characteristics does decision making fall under?
Answer: B
Explanation:
The syllabus definesexplainabilityas the ability to understand how the AI-based system comes up with a particular result:
"Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7)

NEW QUESTION # 123
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION
Answer: D
Explanation:
Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:
A . Clustering is classification of a continuous quantity.
This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.
B . Clustering is supervised learning.
This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.
C . Clustering is done without prior knowledge of output classes.
This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.
D . Clustering requires you to know the classes.
This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.
Therefore, the correct answer is C because clustering is an unsupervised learning technique done without prior knowledge of output classes.

NEW QUESTION # 124
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