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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing
Exam Number:	CT-AI
Related Certifications:	ISTQB CTAL-TTA ISTQB CTFL
Exam Price:	EUR 250
Exam Duration:	60 minutes
Real Exam Qty:	40
Exam Format:	Multiple Choice
Certificate Validity Period:	Lifetime (no expiration)
Available Languages:	English
Passing Score:	65%
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	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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	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 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	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 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.
Topic 7	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
Which option gives the correct values for accuracy and precision from the confusion matrix?
Choose ONE option (1 out of 4)

A. Accuracy = 80%, Precision = 50%
B. Accuracy = 50%, Precision = 75%
C. Accuracy = 80%, Precision = 75%
D. Accuracy = 75%, Precision = 80%

Answer: C

Explanation:
From the confusion matrix:
* True Positives (TP) = 15
* False Positives (FP) = 5
* False Negatives (FN) = 15
* True Negatives (TN) = 65
Accuracy= (TP + TN) / Total
= (15 + 65) / 100
=80%
Precision= TP / (TP + FP)
= 15 / (15 + 5)
= 15 / 20
=75%
Section3.2 - Functional Performance Criteriain the syllabus explains accuracy and precision exactly these ways when evaluating ML classification performance.
Option B is therefore the only correct pair of values.

NEW QUESTION # 89
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

A. Clustering
B. Regression
C. Classification
D. Reinforcement learning

Answer: C

Explanation:
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:
Classification: This type of machine learning involves categorizing input data into predefined classes. In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
Why Not Other Options:
Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.

NEW QUESTION # 90
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

A. Clustering
B. Regression
C. Classification
D. Reinforcement learning

Answer: C

Explanation:
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:
* Classification: This type of machine learning involves categorizing input data into predefined classes.
In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
* Why Not Other Options:
* Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
* Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
* Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.
References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.

NEW QUESTION # 91
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?

A. All high priority defects will be identified using this method
B. The number of parameters to test can be reduced to less than a dozen
C. While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them
D. Pairwise cannot be applied to this problem because there is AI involved and the evolving values may result in unexpected results that cannot be verified

Answer: C

Explanation:
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.2, Page 67 of 99)

NEW QUESTION # 92
Which ONE of the following statements BEST describes how system complexity can cause challenges when testing an AI based system?

A. Unexpected changes in system behaviour can occur
B. It is necessary to measure how changes in the test inputs cause changes in the test outputs
C. Sometimes the system can only be tested as a black-box
D. Obtaining test data is harder

Answer: C

Explanation:
The correct answer is D . The syllabus explains that AI-based systems are often used for tasks too complex for humans to perform, and this can create a test oracle problem because testers may not be able to determine expected results in the usual way. It also states that when the internal structure of an AI-based system is generated by software, the structure may be too complex for humans to understand, leading to the situation where the system can only be tested as a black box.
Option A concerns test data acquisition, which is a general AI testing challenge but not the central complexity mechanism identified in this question. Option B describes an approach used in explainability or bias-related investigation, especially model-agnostic reasoning about input-output sensitivity. Option C is more characteristic of self-learning systems, where the system may change its own behaviour over time.
Complexity primarily causes difficulty because expected behaviour, internal logic, component interactions, and test oracles become hard to understand or define.
References/topics: CT-AI Syllabus Chapter 8, Section 8.5 "Challenges Testing Complex AI-Based Systems"; Section 8.7 "Test Oracles for AI-Based Systems."
=========

NEW QUESTION # 93
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