ISTQB CT-AI Accurate Test | CT-AI Reliable Test Practice

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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 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	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	systems from those required for conventional systems.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?

A. It has been trained to recognize cats and is given an image of a dog
B. It has been trained to analyze mathematical models and is given a set of landscape pictures to classify
C. It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution
D. It has been trained to analyze customer buying trend data and is given information on supplier cost data

Answer: C

Explanation:
The syllabus explains that input changes that arein the same domainas what was used for training are expected to be handled with adaptability:
"Adaptability refers to the ability of a system to adjust its behavior in response to changes in its environment or inputs. This includes changes to the inputs which are still within the expected operational range of the system, such as resolution changes in images or sensor data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.2)

NEW QUESTION # 64
Which ONE of the following statements BEST describes how system complexity can cause challenges when testing an AI-based system?

A. Unexpected changes in system behavior can occur
B. Obtaining test data is harder
C. Sometimes the system can only be tested as a black-box
D. Obtaining test data is harder

Answer: A

Explanation:
Unexpected changes in system behavior can occur due to the complexity of AI-based systems.
These systems often involve many interacting components, which can lead to unpredictable results or variations in performance, making it difficult to anticipate how the system will behave under certain conditions. This presents a significant challenge in testing, as such behavior can be difficult to reproduce or control.

NEW QUESTION # 65
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

A. Selecting testing data from a different dataset than the training dataset
B. Selecting testing data from a boat manufacturer's bolt longevity data
C. Selecting training data by purposely including all known faulty conditions
D. Selecting training data purposely excluding specific faulty conditions

Answer: D

Explanation:
The syllabus defines bias as:
"Bias is the systematic difference in treatment of certain objects, people or groups in comparison to others." It also discusses:
"Sample bias can occur if the data used for training the model does not represent the operational environment, or if some relevant faulty conditions are excluded deliberately." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.3)

NEW QUESTION # 66
Which statement regarding data preparation in the ML workflow is correct?
Choose ONE option (1 out of 4)

A. Since data preparation is time-consuming, all steps should be automated.
B. Sampling is so well researched that it is no longer considered risky.
C. One challenge of data gathering is obtaining high-quality data from multiple sources.
D. A key challenge in data transformation is the removal or correction of erroneous data.

Answer: C

Explanation:
The ISTQB CT-AI syllabus describes theML data preparation workflowin Section2.2 - Data Preparation.
Data preparation consists ofdata gathering,cleaning,transformation, andsampling. The syllabus emphasizes that one significant challenge duringdata gatheringis combining data frommultiple heterogeneous sources, which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow.
This aligns directly with OptionC.
Option A is incorrect because erroneous data correction is part ofcleaning, not transformation. Option B contradicts the syllabus: while automation can help,not all steps should be automateddue to the need for expert oversight, especially in detecting subtle data quality issues. Option D is incorrect because sampling continues to involve risk-particularly around representativeness-and the syllabus emphasizes caution, not complacency.
Thus, OptionCis the only statement that accurately reflects the syllabus.

NEW QUESTION # 67
Which of the following problems would best be solved using the supervised learning category of regression?

A. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.
B. Determining if an animal is a pig or a cow based on image recognition.
C. Recognizing a knife in carry on luggage at a security checkpoint in an airport scanner.
D. Determining the optimal age for a chicken's egg laying production using input data of the chicken's age and average daily egg production for one million chickens.

Answer: D

Explanation:
The syllabus states:
"Supervised learning... divides problems into two categories: classification and regression.
Regression is used when the problem requires the ML model to predict a numeric output, for example predicting the age of a person based on their habits."

NEW QUESTION # 68
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