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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing Exam
Exam Number:	CT-AI
Available Languages:	Portuguese, Spanish, English, Chinese, Japanese, French, German, Korean
Real Exam Qty:	40
Related Certifications:	ISTQB Certified Tester Testing with Generative AI (CT-GenAI) ISTQB Certified Tester Foundation Level (CTFL)
Exam Format:	Multiple-choice questions, 1-2 points per question
Exam Price:	€180 - €250 (varies by region and provider)
Exam Duration:	60 (75 for non-native language)
Passing Score:	65% (29/44 points for v2.0; 31/47 points for v1.0)
Certificate Validity Period:	Valid indefinitely (no expiration)
Recommended Training:	CT-AI Syllabus v2.0 ISTQB Accredited Training Providers
Exam Registration:	iSQI Exam Registration ISTQB Official Registration Pearson VUE
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online remote proctored / Onsite test center
Pre Condition:	Must hold ISTQB Certified Tester Foundation Level (CTFL) certification
Official Syllabus URL:	https://istqb.org/certifications/certified-tester-ai-testing-ct-ai/

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	systems from those required for conventional systems.
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	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 (Q91-Q96):

NEW QUESTION # 91
Which ONE of the following describes a situation of back-to-back testing the LEAST?

A. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
B. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
C. Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data
D. Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.

Answer: A

Explanation:
Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions.
Let's analyze the options given:
A). Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.
B). Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for the same data.
This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.
C). Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.
D). Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn't involve comparing multiple implementations.
Based on this analysis, option C is the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to- back testing.

NEW QUESTION # 92
Which of the following options is an example of the concept of overfitting?

A. A model for predicting IT system failures delivers too many false-negative predictions because the failures cannot be adequately explained via the log files used for training.
B. A model for the recognition of dogs was trained predominantly with pictures of dogs in parks. On pictures with other animals in parks, dogs are also falsely recognized.
C. A model for predicting academic performance was trained with data from students at one university. The model shows low predictive accuracy when applied to other universities.
D. A previously trained model for recognizing cars is adapted and extended so that it can also identify the make of the car beyond its original function.

Answer: C

Explanation:
The ISTQB CT-AI syllabus defines overfitting in Section3.2 - ML Model Evaluationas a condition where an ML model learns the training data too precisely--including noise and irrelevant detail-- resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. Option A perfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from other universities.
This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across environments .

NEW QUESTION # 93
Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem?
SELECT ONE OPTION

A. Bias issues
B. Privacy issues
C. Security issues
D. Accuracy issues

Answer: D

Explanation:
The question refers to a problem where data used for an object detection ML system was labelled incorrectly. This issue is most closely related to "accuracy issues." Here's a detailed explanation:
Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly impacts the model's accuracy, leading to poor performance because the model learns incorrect patterns.
Why Not Other Options:
Security Issues: This pertains to data breaches or unauthorized access, which is not relevant to the problem of incorrect data labeling.
Privacy Issues: This concerns the protection of personal data and is not related to the accuracy of data labeling.
Bias Issues: While bias in data can affect model performance, it specifically refers to systematic errors or prejudices in the data rather than outright incorrect labeling.

NEW QUESTION # 94
Which of the following is a problem with AI-generated test cases that are generated from the requirements?

A. They are defect prone because they are unable to detect nuances in the requirements.
B. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures.
C. They are slow and will usually not be able to execute in the time allowed.
D. They make debugging more complicated because the number of steps is usually high in order to induce the target failure.

Answer: B

Explanation:
AI-generated test cases are often created using machine learning (ML) models or heuristic algorithms. While these can be effective in generating large numbers of test cases quickly, they often suffer from the "test oracle problem." Test Oracle Problem: A test oracle is the mechanism used to determine the expected output of a test case. AI-generated test cases often lack expected results because AI-based tools do not inherently understand what the correct output should be.
Difficulty in Verification: Without expected results, verifying test cases becomes challenging.
Testers must rely on heuristics, anomaly detection, or significant failures, rather than traditional pass/fail conditions.

NEW QUESTION # 95
Which of the following technologies for implementing AI is considered to be a reasoning technique?

A. Genetic algorithms
B. Deductive classifiers
C. Linear regression
D. Random Forest

Answer: B

Explanation:
The ISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is"Reasoning techniques."These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning.
Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.
Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.
The other options--Linear regression,Random Forest, and Genetic algorithms--are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.
Thus, based on the authoritative definitions in the syllabus, Deductive classifiers (Option A)is the only technology classified as a reasoning technique.

NEW QUESTION # 96
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