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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing
Exam Number:	CT-AI
Exam Duration:	60 minutes
Passing Score:	65%
Available Languages:	English
Exam Format:	Multiple Choice
Real Exam Qty:	40
Certificate Validity Period:	Lifetime (no expiration)
Related Certifications:	ISTQB CTAL-TTA ISTQB CTFL
Exam Price:	EUR 250
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	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 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	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 5	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Topic 6	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 7	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 8	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 9	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 10	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 (Q63-Q68):

NEW QUESTION # 63
Which ONE of the below statements BEST describes why test environments for autonomous systems might need to be different to other test environments?

A. Tools may be required to provide explanations of system behaviour
B. Tools may be required to simulate extreme scenarios
C. Non determinism may need to be introduced into the environment
D. AI-specific hardware may be required

Answer: B

Explanation:
The correct answer is A . For autonomous systems, the distinguishing test-environment issue is that the system must respond to changes in its environment without human intervention and must also recognize situations where autonomy should be ceded back to human operators. The CT-AI syllabus states that, for some autonomous systems, identifying and mimicking the circumstances for ceding autonomy may require test environments to push the systems to extremes . It also notes that some autonomous systems operate in hazardous environments, making representative real-world testing difficult or unsafe.
Option B relates more specifically to multi-agent AI systems, where the environment may need non- determinism to mimic other interacting AI-based systems. Option C is a general AI-environment consideration where AI-specific processors may need inclusion. Option D is associated with explainability, where tools may be needed to understand decisions. These are valid AI test-environment factors, but they are not the best autonomy-specific reason. Virtual environments are particularly valuable because dangerous, unusual, and extreme scenarios can be tested safely and repeatedly.
References/topics: CT-AI Syllabus Chapter 10, Sections 10.1 and 10.2 "Test Environments for AI-Based Systems."
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NEW QUESTION # 64
Which statement regarding flexibility and adaptability of AI-based systems is correct?
Choose ONE option (1 out of 4)

A. Adaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.
B. Flexibility is considered to be the ease with which the system can be reprogrammed to a changed operating condition.
C. Adaptability is considered to be the ability of the system to be used in unspecified situations.
D. Self-learning AI-based systems are classified according to whether they are adaptable only or flexible only.

Answer: A

Explanation:
The ISTQB CT-AI syllabus defines these two concepts clearly inSection 2.1 - Flexibility and Adaptability.
Flexibility is described as the ability of a system to operate in situationsnot explicitly covered in its original requirements, while adaptability refers to how easily the system can bemodifiedto handle new environments or conditions. The syllabus stresses that both flexibility and adaptability are crucial, particularly inself- learning AI systemsthat may need to respond to changes in their environment and adjust their behavior accordingly. It states that systems must be capable of determiningwhenandhowto adjust behavior in evolving situations, especially when the operational environment is not fully known at deployment time . This directly aligns with OptionA.
Option B reverses definitions-the syllabus states flexibility (not adaptability) relates to unspecified situations. Option C is incorrect: self-learning systems requirebothflexibility and adaptability; they are not categorized as one or the other. Option D incorrectly defines flexibility; the syllabus defines adaptability-not flexibility-as ease of modification.
Thus,Option Acorrectly reflects the syllabus.

NEW QUESTION # 65
A facial recognition system is being deployed at airports in order to scan passengers' faces and compare them to a database of vaccinations, in order to identify unvaccinated passengers in a pandemic. There are a number of components involved including cameras, a model to segment the image, and a model to identify the face and match it against a known photograph. It is important that there are few false negatives, and that passengers cannot subvert the system. Which ONE of the following types of testing is the MOST appropriate options for the tests you would choose in system testing?

A. Adversarial testing
B. Testing for transparency
C. Testing for explainability
D. Testing for concept drift

Answer: A

Explanation:
The correct answer is B. Adversarial testing . The key risk in this scenario is deliberate subversion of the facial recognition process. The syllabus defines adversarial attacks as situations where an attacker subtly perturbs valid inputs passed to a trained model so that it produces incorrect predictions. It specifically notes that image classifiers can be misled by changes that may be invisible to the human eye, causing the model to classify an image incorrectly with high confidence.
This airport system uses cameras, segmentation, and face-matching models, so adversarial inputs could include manipulated facial images, masks, presentation attacks, lighting manipulation, or other perturbations intended to create false negatives. The syllabus states that adversarial testing performs adversarial attacks to identify vulnerabilities so that preventive measures can be taken. It also identifies adversarial testing as appropriate where mishandling adversarial examples could have significant impact or where the system may be attacked.
References/topics: CT-AI Syllabus Chapter 9, Sections 9.1.1 and 9.7; Chapter 7, Section 7.2.5 "System Testing."
=========

NEW QUESTION # 66
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. Static analysis
B. State transition testing
C. Equivalence partitioning
D. Exploratory testing

Answer: D

Explanation:
The syllabus describes exploratory testing as especially useful when there are poor specifications or test oracle problems:
"Exploratory testing is especially useful when there are poor specifications or test oracle problems, which is often the case for AI-based systems." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.6, page 70 of 99)

NEW QUESTION # 67
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

A. Grouping similar products together before feeding them into the algorithm
B. Minimizing the amount of time spent training the algorithm
C. Labeling the data correctly
D. Selecting the correct data pipeline for the ML training

Answer: C

Explanation:
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1)

NEW QUESTION # 68
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