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ISTQB CT-AI Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 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 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 5

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 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

Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?

A. The robot must recharge for no more than six hours a day
B. The robot must complete 99.99% of its deliveries each day
C. The robot must record the time of each delivery which is compiled into a report
D. The robot must evolve to optimize its routing

Answer: D

Explanation:
AI-based systems have specific quality characteristics, includingevolution,autonomy, andadaptability. A test objective that evaluates whether an AI systemevolvesto improve performance over time directly aligns with AI quality characteristics.
Explanation of Answer Choices:
* Option A: The robot must evolve to optimize its routing.
* Correct.Evolution is an AI quality characteristic that ensures the systemlearns from past experiencesand adapts to improve efficiency.
* Option B: The robot must recharge for no more than six hours a day.
* Incorrect.This is an operational constraint rather than an AI-specific quality characteristic.
* Option C: The robot must record the time of each delivery which is compiled into a report.
* Incorrect.Logging data does not relate to AI quality characteristics likeadaptability or autonomy.
* Option D: The robot must complete 99.99% of its deliveries each day.
* Incorrect.This is a performance target rather than an AI quality characteristic.
ISTQB CT-AI Syllabus References:
* Evolution as an AI Quality Characteristic:"Check how well the system learns from its own experience. Check how well the system copes when the profile of data changes (i.e., concept drift)".
Thus,Option A is the best choice as it directly tests an AI quality characteristic (evolution) in the upgraded autonomous robot.

NEW QUESTION # 21
Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?
SELECT ONE OPTION

A. Test for human handover to give rest to the system.
B. Test for human handover requiring mandatory relinquishing control.
C. Test for human handover when it should actually not be relinquishing control.
D. Test for human handover after a given time interval.

Answer: C

Explanation:
* Testing Autonomy: Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems. The other tests are relevant to ensuring smooth operation and transitions between human and AI control.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Testing Autonomous AI-Based Systems and Testing for Human-AI Interaction.

NEW QUESTION # 22
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates.
How could AI help make the automation more reliable?

A. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.
B. It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.
C. It could modify the automation code to ignore unrecognizable objects to avoid failures.
D. It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.

Answer: D

Explanation:
Object recognition issues in test automation often arise whendevelopers frequently change object identifiers in the GUI. AI can enhance the stability of GUI automation by:
* Using multiple criteria for object identification
* AI cantrack UI elements using multiple attributessuch asXPath, label, ID, class, and screen coordinatesrather than relying on a single identifier that may change over time.
* This approach makes the automationless brittle and more adaptive to changes in the UI.
* Why other options are incorrect?
* B (Ignore unrecognizable objects to avoid failures): Ignoring objects instead of identifying them properly wouldlead to incomplete or incorrect test execution.
* C (Dynamically name objects and alter source code): AI-based testing tools donot modify application source code; they work byadjusting the recognition strategy.
* D (Anticipate developer changes and pre-alter automation code): While AI can adapt,it does not predict future changes to the GUI, making this option unrealistic.
Thus,Option A is the best answer, as AI tools enhance object recognitionby dynamically selecting the most stable and persistent identification methods, improving test automation reliability.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 11.6.1 (Using AI to Test Through the Graphical User Interface (GUI))
* ISTQB CT-AI Syllabus v1.0, Section 11.6.2 (Using AI to Test the GUI).

NEW QUESTION # 23
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?

A. Equivalence partitioning
B. Exploratory testing
C. Static analysis
D. State transition testing

Answer: B

Explanation:
Whentesting an AI-based chat assistantfor e-commerce customers, thelack of sufficient specifications makes it difficult to use structured test techniques. TheISTQB CT-AI Syllabusrecommendsexploratory testingin such cases:
* Why Exploratory Testing?
* Exploratory testing is usefulwhen specifications are incomplete or unclear.
* AI-based systems, particularly those usingnatural language processing (NLP),may not behave deterministically, making scripted test cases ineffective.
* Thetester interacts dynamicallywith the system, identifying unexpected behaviorsnot documented in the specification.
* Analysis of Answer Choices:
* A (Exploratory testing)#Correct, as it is the best approach when specifications are incomplete.
* B (Static analysis)# Incorrect, as static analysis checks code without execution, which isnot helpfulfor AI chatbots.
* C (Equivalence partitioning)# Incorrect, asthis technique requires well-defined inputs and outputs, which are missing due toinsufficient specifications.
* D (State transition testing)# Incorrect, as state-based testingrequires knowledge of valid and invalid transitions, which is difficult with a chatbot lacking a clear specification.
Thus,Option A is the correct answer, asexploratory testing is the best approach when dealing with insufficient specifications in AI-based systems.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 7.7 (Selecting a Test Approach for an ML System)
* ISTQB CT-AI Syllabus v1.0, Section 9.6 (Experience-Based Testing of AI-Based Systems).

NEW QUESTION # 24
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

A. The quality of the labeling
B. The number of classes
C. Biased data
D. The data pipeline

Answer: B

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 # 25
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