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Quiz 2025 CT-AI: Certified Tester AI Testing Exam Fantastic Certification Exam Infor

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ISTQB CT-AI Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 4

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 5

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 6

systems from those required for conventional 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

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

Topic 9

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 10

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.

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Realistic ISTQB CT-AI Exam Questions

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION

A. A lack of similarity between the training and testing data.
B. A lack of focus on choosing the right functional-performance metrics.
C. The input data has not been tested for quality prior to use for testing.
D. A lack of focus on non-functional requirements testing.

Answer: A

Explanation:
The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.
* A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.
* The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.
* A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.
* A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.
:
ISTQB CT-AI Syllabus Section 4.2 on Training, Validation, and Test Datasets emphasizes the importance of using representative datasets to ensure the model generalizes well to real-world data.
Sample Exam Questions document, Question #40 addresses issues related to data representativeness and model generalization.

NEW QUESTION # 82
A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options.
When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market the only response from the automated system is: "Idon't understand your question." This AI system should be categorized as?

A. Super AI
B. General AI
C. Conventional AI
D. Narrow AI

Answer: D

Explanation:
Narrow AI (also known as Weak AI) is designed to perform specific tasks without possessing general intelligence or consciousness. The AI system in the question is capable of recognizing speech and responding to specific booking-related requests but fails when asked about unrelated topics (such as weather or labor markets).
* Option A:"General AI"
* Incorrect. General AI (AGI) refers to an AI system that can perform any intellectual task a human can. The described system is task-specific and does not exhibit general intelligence.
* Option B:"Narrow AI"
* Correct. The AI system is limited to a predefined domain (ticket booking) and cannot process unrelated questions. This is characteristic of Narrow AI, which excels at specific tasks but lacks broader cognitive abilities.
* Option C:"Super AI"
* Incorrect. Super AI surpasses human intelligence, exhibiting advanced reasoning and creativity.
The AI in the scenario is far from this level.
* Option D:"Conventional AI"
* Incorrect. Conventional AI is a broader term that may include rule-based systems. The described system relies on machine learning and natural language processing, making it more aligned with Narrow AI.
* Definition of Narrow AI:"Narrow AI refers to AI systems that are designed to perform a single task or a limited set of tasks, without general intelligence".
* General vs. Narrow AI:"General AI remains an area of research, while most current AI applications fall into the category of Narrow AI".
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,option B is the correct categorization for the AI-based ticket booking system.

NEW QUESTION # 83
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

A. Reinforcement learning
B. Regression
C. Clustering
D. Classification

Answer: D

Explanation:
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:
* Classification: This type of machine learning involves categorizing input data into predefined classes.
In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
* Why Not Other Options:
* Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
* Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
* Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.
References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.

NEW QUESTION # 84
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 complete 99.99% of its deliveries each day
B. The robot must recharge for no more than six hours a day
C. The robot must evolve to optimize its routing
D. The robot must record the time of each delivery which is compiled into a report

Answer: C

Explanation:
In the syllabus, theevolutioncharacteristic for AI-based systems means the ability of the system to evolve and adapt its behavior in response to changes in the environment or in its own performance:
"Evolution is the system's ability to change itself to adapt to new situations, different hardware, or a changing operational environment." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.3)

NEW QUESTION # 85
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION

A. Al systems are inherently flexible.
B. Flexible Al systems allow for easier modification of the system as a whole.
C. Al systems require changing of operational environments; therefore, flexibility is required.
D. Self-learning systems are expected to deal with new situations without explicitly having to program for it.

Answer: B

Explanation:
Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.
* AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.
* AI systems require changing operational environments; therefore, flexibility is required (B):
While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.
* Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.
* Self-learning systems are expected to deal with new situations without explicitly having to program for it (D): This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.
Hence, the correct answer isC. Flexible AI systems allow for easier modification of the system as a whole.
:
ISTQB CT-AI Syllabus Section 2.1 on Flexibility and Adaptability discusses the importance of flexibility in AI systems and how it enables easier modification and adaptability to new situations.
Sample Exam Questions document, Question #30 highlights the importance of flexibility in AI systems.

NEW QUESTION # 86
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