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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing
Exam Number:	CT-AI
Available Languages:	English
Related Certifications:	ISTQB CTAL-TTA ISTQB CTFL
Real Exam Qty:	40
Exam Price:	EUR 250
Exam Format:	Multiple Choice
Passing Score:	65%
Exam Duration:	60 minutes
Certificate Validity Period:	Lifetime (no expiration)
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online proctored or in-person at authorized testing centers
Pre Condition:	ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory
Official Syllabus URL:	https://www.istqb.org/certifications/artificial-intelligence-testing-certification

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 2	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 3	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 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	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 6	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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q76-Q81):

NEW QUESTION # 76
Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?
SELECT ONE OPTION

A. Feature selection
B. Data labeling
C. Data augmentation
D. Data sampling

Answer: D

Explanation:
A . Feature selection
Feature selection is the process of selecting the most relevant features from the data. While important, it is not directly about handling excess data.
B . Data sampling
Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.
C . Data labeling
Data labeling involves annotating data for supervised learning. It is necessary for training models but does not address the issue of having excess data.
D . Data augmentation
Data augmentation is used to increase the size of the training dataset by creating modified versions of existing data. It is useful when there is insufficient data, not when there is excess data.
Therefore, the correct answer is B because data sampling is the most relevant activity when dealing with an excess amount of data for training.

NEW QUESTION # 77
Which statement about automation bias is correct?
Choose ONE option (1 out of 4)

A. Automation bias is tested with representative users, but human input quality is irrelevant
B. Automation bias affects the testing of AI-based systems that support users in their actions or decisions
C. Automation bias particularly affects testing of autonomous systems
D. When testing AI-based systems, automation bias does not play a role in supporting test activities such as boundary value analysis

Answer: B

Explanation:
Automation bias is defined in Section4.4 - Human Factors in AI Testingof the ISTQB CT-AI syllabus. It refers to the human tendency to overly trust, rely on, or defer to automated system outputs. The syllabus explains that this bias arises especially indecision-support systems, where humans may accept AI judgments without adequate verification. This aligns directly with Option B.
Option A is incorrect: automation biasdoesinfluence testing, especially when testers rely excessively on AI outputs. The syllabus cautions about testers adopting the same cognitive biases as end users. Option C is incorrect because autonomous systems are not the primary context; rather,systems supporting human decisionsare most impacted. Option D is incorrect because the quality of human inputmatters significantly, and poorly designed user studies can mask or distort automation bias.
Thus,Option Bis the syllabus-accurate description of automation bias.

NEW QUESTION # 78
A test engineer is planning the best functional performance metrics to evaluate an unsupervised learning model. The model groups data points based on their similarity. The test engineer wants to measure how similar the data points in each group actually are. Which is the MOST likely metric they should use:

A. ROC
B. AUC
C. Intra cluster
D. Recall

Answer: C

Explanation:
The correct answer is B. Intra cluster . The scenario describes an unsupervised clustering model, where the objective is to group data points based on similarity. The requested measurement is specifically how similar the data points are within each group , which maps directly to intra-cluster metrics . The CT-AI syllabus states that inter-cluster metrics, intra-cluster metrics, and the silhouette coefficient may be used for unsupervised clustering problems.
Option A, ROC, and option C, AUC, are used for supervised classification, especially binary classifiers, to evaluate how well the classifier distinguishes between classes. Option D, recall, is also a supervised classification metric and measures how many actual positives were correctly predicted. None of those metrics is designed to evaluate compactness or similarity within clusters. Intra-cluster measurement is the most direct metric family because it evaluates the cohesion of a cluster: the closer or more similar the members are, the stronger the clustering result is likely to be.
References/topics: CT-AI Syllabus Chapter 5, Section 5.2 "Additional ML Functional Performance Metrics for Classification, Regression and Clustering"; Section 5.4 "Selecting ML Functional Performance Metrics."
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NEW QUESTION # 79
Which ONE of the following requirements for an AI-based system is MOST likely to cause a challenge for testing?

A. The system should respond more quickly than the system it is replacing
B. The system should be able to accept natural language input on a variety of topics and respond correctly
C. The system should not require human intervention until a specific failure scenario is encountered
D. The system should be 100% accurate with no tolerance for failure

Answer: B

Explanation:
Testing an AI-based system that accepts natural language input on a variety of topics and responds correctly is particularly challenging because natural language processing (NLP) involves handling vast variability in language, context, and possible interpretations. This makes it difficult to define precise expected outcomes and test all possible inputs and responses.

NEW QUESTION # 80
Which of the following descriptions of quality aspects of a data set is correct?

A. The quality aspect "Unbalanced data" describes the fact that the data used should be as up-to- date as possible.
B. The quality aspect "Data not preprocessed" describes the fact that the collected data was recorded incorrectly.
C. The quality aspect "Irrelevant data" describes the fact that irrelevant data does not affect the ML model.
D. The quality aspect "Incomplete data" describes the fact that data is missing, e.g., for a certain time interval.

Answer: D

Explanation:
The ISTQB CT-AI syllabus describes severaldata quality aspectsthat affect ML performance. In Section2.2 - Data Preparation, it explains that datasets may suffer from issues such as incomplete data, irrelevant data,incorrect data, unbalanced data, or data lacking preprocessing.
"Incomplete data" means thatportions of the required data are missing, often because some time periods, records, or sources were not captured. This aligns exactly with Option A, which correctly identifies missing intervals as incomplete data.

NEW QUESTION # 81
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