ISTQB CT-AI Valid Exam Braindumps, Reliable CT-AI Exam Materials

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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing
Exam Number:	CT-AI
Available Languages:	English
Exam Price:	EUR 250
Certificate Validity Period:	Lifetime (no expiration)
Passing Score:	65%
Related Certifications:	ISTQB CTAL-TTA ISTQB CTFL
Real Exam Qty:	40
Exam Duration:	60 minutes
Exam Format:	Multiple Choice
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

>> ISTQB CT-AI Valid Exam Braindumps <<

Free PDF Quiz ISTQB - CT-AI - Certified Tester AI Testing Exam Accurate Valid Exam Braindumps

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ISTQB CT-AI Exam Syllabus Topics:

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

ISTQB Certified Tester AI Testing Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
An airline has created an ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in, the load weights became less accurate. After some exploratory data analysis, it became apparent that luggage weights were higher in the winter than in summer.
Which of the following statements BEST describes the problem and how it could have been prevented?

A. The model suffers from corruption and therefore should be reloaded into the computer system being used, preferably with a method of version control to prevent further changes
B. The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated
C. The model suffers from drift and therefore the performance standard should be eased until a new model with more transparency can be developed
D. The model suffers from a lack of transparency and therefore should be regularly tested to ensure that any progressive errors are detected soon enough for the problem to be mitigated

Answer: B

Explanation:
The syllabus states:
"Concept drift occurs when the operational environment changes without the trained model changing correspondingly. The outputs of the model become less accurate and less useful. Therefore, the operational model should be regularly evaluated against its acceptance criteria." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6, Page 54 of 99)

NEW QUESTION # 33
Which of the following descriptions of quality aspects of a data set is correct?

A. The quality aspect "Irrelevant data" describes the fact that irrelevant data does not affect the ML model.
B. The quality aspect "Data not preprocessed" describes the fact that the collected data was recorded incorrectly.
C. The quality aspect "Incomplete data" describes the fact that data is missing, e.g., for a certain time interval.
D. The quality aspect "Unbalanced data" describes the fact that the data used should be as up-to- date as possible.

Answer: C

Explanation:
The ISTQB CT-AI syllabus describes severaldata quality aspectsthat affect ML performance. In Section2.2 - Data Preparation, it explains that datasets may suffer from issues such as incomplete data, irrelevant data,incorrect data, unbalanced data, or data lacking preprocessing.
"Incomplete data" means thatportions of the required data are missing, often because some time periods, records, or sources were not captured. This aligns exactly with Option A, which correctly identifies missing intervals as incomplete data.

NEW QUESTION # 34
You have been asked for your opinion on the ML approach to be used for a new age verification system. A training dataset is available with many pictures of faces and the subject's age. When new users sign up to a mobile app, they have to take a picture of their face. If the system believes they are under 18 years old it will ask them to provide a picture of their passport to prove their age. Which ONE of the following approaches do you expect to be MOST likely to succeed?

A. Supervised learning regression system that is re trained based on new data, including whether they provided ID that showed their age.
B. Reinforcement learning classification system with a reward function of correct behaviour.
C. Supervised learning classification system that is re-trained based on new data, including whether they later provided ID that showed they were over 18.
D. Unsupervised learning that identifies clusters in training data that relate to people's ages.

Answer: C

Explanation:
The correct answer is B . The operational decision required by the system is categorical: the user is either treated as under 18 or not under 18 for the purpose of requesting passport evidence. The CT-AI syllabus states that classification is used when an input must be assigned to one of a few predefined classes, and that supervised learning requires labelled data. Here, the training dataset contains facial images and ages, so it can be converted into labelled classes such as "under 18" and "18 or over." Option A is weaker because unsupervised learning is used where labels are not provided and the goal is to identify patterns or clusters. In this case, useful labels already exist. Option C could predict a numeric age using regression, but the business decision is binary, so classification is the more direct and controlled approach. Option D is inappropriate because reinforcement learning is better suited to sequential decision- making through interaction with an environment, states, and rewards. The passport feedback provides useful labelled evidence for retraining, supporting supervised classification.
References/topics: CT-AI Syllabus Chapter 3, Sections 3.1.1 and 3.3 "Selecting a Form of ML."
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NEW QUESTION # 35
Which ONE of the following requirements for an AI based system is MOST likely to cause a challenge for testing?

A. The system should be 100% accurate with no tolerance for failure
B. The system should be able to accept natural language input on a variety of topics and respond correctly
C. The system should respond more quickly than the system it is replacing
D. The system should not require human intervention until a specific failure scenario is encountered

Answer: A

Explanation:
The correct answer is C . A requirement that an AI-based system be 100% accurate with no tolerance for failure creates a major testing challenge because many AI-based systems are probabilistic and may require accuracy tolerances rather than absolute expected outcomes. The CT-AI syllabus states that the probabilistic nature of many AI-based systems can make it necessary to specify tolerances for quality requirements such as prediction accuracy.
Option A is also challenging because natural language interfaces increase flexibility and make it difficult to document all possible interactions, but it is not as absolute as requiring perfect accuracy. Option B is a conventional performance efficiency requirement and can normally be tested using timing and resource measurements. Option D relates to autonomy and may require scenario-based testing, but it is still testable if the operational envelope and failure conditions are well specified. The decisive issue with option C is that it provides no tolerance range, no probabilistic acceptance model, and no practical allowance for statistical uncertainty. This creates both a specification problem and a test oracle problem.
References/topics: CT-AI Syllabus Chapter 7, Section 7.1 "Specification of AI-Based Systems"; Chapter
8, Section 8.4 "Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems."
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NEW QUESTION # 36
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. State transition testing
D. Static analysis

Answer: B

NEW QUESTION # 37
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