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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	systems from those required for conventional systems.
Topic 3	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 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	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 6	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 7	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 8	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 9	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.

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Quiz ISTQB - Valid CT-AI - Certified Tester AI Testing Exam Reliable Test Objectives

There are Certified Tester AI Testing Exam (CT-AI) exam questions provided in Certified Tester AI Testing Exam (CT-AI) PDF questions format which can be viewed on smartphones, laptops, and tablets. So, you can easily study and prepare for your Certified Tester AI Testing Exam (CT-AI) exam anywhere and anytime. You can also take a printout of these ISTQB PDF Questions for off-screen study.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q138-Q143):

NEW QUESTION # 138
You are a test manager planning testing for an invoice financing company. The company buy unpaid invoices from companies and provide them with immediate cash. The company is replacing their existing conventional system, which takes company accounting records as inputs, with an ML system that classifies each invoice for sales as something that should, or should not be bought. Significant historical production data is available. It is important that invoices are not bought incorrectly. Which ONE of the following test techniques would be MOST appropriate for you to plan for system testing?

A. Metamorphic testing
B. A/B testing
C. Back-to-back testing
D. Experience-based testing

Answer: C

Explanation:
The correct answer is B. Back-to-back testing . The scenario contains the key conditions for this technique:
an existing conventional system with similar functionality, a new ML-based replacement, and significant historical production data. The CT-AI syllabus explains that back-to-back testing uses an alternative version of the system as a pseudo-oracle , comparing its outputs with those produced by the system under test. The pseudo-oracle may be an existing system or even conventional non-AI software.
This is stronger than A/B testing because the objective is not primarily to compare two production variants statistically to determine which is better; it is to detect classification defects in a replacement system. It is also stronger than metamorphic testing because an existing oracle-like comparator is available, reducing the need to rely only on relative output relations. Experience-based testing is useful as a supplement, but it would not be the primary systematic technique where historical production inputs and an existing system are available.
References/topics: CT-AI Syllabus Chapter 9, Section 9.3 "Back-to-Back Testing"; Section 9.7
"Selecting Test Techniques for AI-Based Systems."
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NEW QUESTION # 139
Which ONE of the following statements is MOST true about black box adversarial testing?

A. Combinatorial testing techniques are used
B. The approach relies on the transferability of the attacks
C. The algorithm used for the model is known to the tester
D. The training data is manipulated

Answer: B

Explanation:
The correct answer is B . The CT-AI syllabus explains that black-box adversarial attacks involve exploring the model to determine its functionality and then building a duplicate model with similar functionality. The attacker then uses a white-box approach on the duplicate model to identify adversarial examples. Because adversarial examples are generally transferable, those examples will normally also work on the original model.
Option A describes white-box adversarial testing, where the algorithm, model settings, and parameters are known. Option C describes data poisoning , where training data is manipulated to induce incorrect future predictions or create vulnerabilities. Option D describes pairwise or broader combinatorial testing, which is used where many parameters and values make exhaustive testing impractical. Black-box adversarial testing is instead based on limited knowledge of the target model and the empirical transferability of adversarial examples. If a duplicate model cannot be built, the syllabus also notes that high-volume automated testing may be used to discover adversarial examples and observe results.
References/topics: CT-AI Syllabus Chapter 9, Section 9.1.1 "Adversarial Attacks"; Section 9.1.2 "Data Poisoning."
=========

NEW QUESTION # 140
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?

A. Machine learning on logs of execution
B. Natural language processing on textual requirements
C. Analyzing source code for generating test cases
D. GUI analysis by computer vision

Answer: B

Explanation:
Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases.
This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.

NEW QUESTION # 141
A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not.
The bank has enough data on past customers to randomly split the data into a training dataset and a test
/validation dataset. A logistic regression model is constructed on the training dataset using the following independent variables:
* Gender
* Marital status
* Number of dependents
* Education
* Income
* Loan amount
* Loan term
* Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?

A. A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model.
B. Acceptance testing should be used to make sure the algorithm is suitable for the customer. The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation dataset ensuring no bias is present.
C. Experience-based testing should be used to confirm that the training data set is operationally relevant.
This can include applying exploratory data analysis (EDA) to check for bias within the training data set.
D. Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set. If the two models significantly differ, it will indicate there is bias in the original model.

Answer: C

Explanation:
The syllabus mentions that experience-based testing and EDA are effective for detecting biases:
"Experience-based testing can be used to verify that the training dataset is operationally relevant and identify potential sources of bias. EDA is also useful for exploring the data and understanding any relationships that might lead to bias in the model." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.3, page 58 of 99)

NEW QUESTION # 142
Which of the following statements regarding experience-based testing for AI-based systems is correct?

A. Intuitive test case design for AI-based systems involves interactive, hypothesis-driven examination of data for correlations or developmental trends.
B. Exploratory testing is often used for AI-based systems because there are often insufficient specifications or problems with the test oracle for AI-based systems.
C. In checklist-based testing of AI-based systems, the existing test cases are dynamically adapted, for example based on metamorphic testing.
D. Tour refers to intuitive test case design for AI-based systems based on multiple, sequential test cases using systematically biased training data.

Answer: B

Explanation:
The ISTQB CT-AI syllabus explains inSection 4.4 - Experience-Based Testing for AI Systemsthat AI-based systems frequently suffer frominsufficient specifications, unpredictable model behavior, andtest oracle problems, especially when outputs depend on probabilistic or learned patterns.
The syllabus explicitly states thatexploratory testingis especially valuable in such contexts because it allows testers to investigate the system interactively, observe unexpected behavior, and evaluate system responses that cannot be fully predicted beforehand. Thus, Option C accurately reflects the role and justification of exploratory testing for AI systems.

NEW QUESTION # 143
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