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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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	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 5	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 6	systems from those required for conventional systems.
Topic 7	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 8	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 9	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 10	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 11	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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q109-Q114):

NEW QUESTION # 109
A transportation company operates three types of delivery vehicles in its fleet. The vehicles operate at different speeds (slow, medium, and fast). The transportation company is attempting to optimize scheduling and has created an AI-based program to plan routes for its vehicles using records from the medium-speed vehicle traveling to selected destinations. The test team uses this data in metamorphic testing to test the accuracy of the estimated travel times created by the AI route planner with the actual routes and times.
Which of the following describes the next phase of metamorphic testing?

A. The team uses the same AI route planner to create routes that are longer and shorter but follow the same track. Finally, by driving the fast vehicles on the long routes and slow vehicles on the short routes and vice versa, the AI system will have enough information to infer travel times for all vehicles on all routes.
B. The team uses an AI system to select the most dissimilar routes. With this information, any of the AI routes can be metaphorically transformed into a fast or slow route.
C. The team tests the time required for the fast and slow vehicles to travel the same route as the medium vehicle. Then, by calculating the speed difference, they then predict how much faster or slower the vehicles will travel. That information is then used to verify that the arrival time of the vehicles meets the expected result.
D. The team decomposes each route into the relevant components that affect the travel time such as traffic density and vehicle power. The team then uses statistical analysis to characterize the influence of each component to calculate the fast and slow vehicle route times.

Answer: C

Explanation:
Metamorphic Testing (MT) is a testing technique that verifies AI-based systems by generating follow-up test cases based on existing test cases. These follow-up test cases adhere to a Metamorphic Relation (MR), ensuring that if the system is functioning correctly, changes in input should result in predictable changes in output.
Metamorphic testing works by transforming source test cases into follow-up test cases Here, the source test case involves testing the medium-speed vehicle's travel time. The follow-up test cases are derived by extrapolating travel times for fast and slow vehicles using predictable relationships based on speed differences.
MR states that modifying input should result in a predictable change in output Since the speed of the vehicle is a known factor, it is possible to predict the new arrival times and verify whether they follow expected trends.
This is a direct application of metamorphic testing principles In route optimization systems, metamorphic testing often applies transformations to speed, distance, or conditions to verify expected outcomes.

NEW QUESTION # 110
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 identify the objects multiple ways and then determine the most commonly used and stable identification for each object.
B. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.
C. It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.
D. It could modify the automation code to ignore unrecognizable objects to avoid failures.

Answer: A

NEW QUESTION # 111
Which of the following statements about bias in AI based systems is MOST correct?

A. Inappropriate bias can be caused by aspects of the algorithm or the data
B. Inappropriate bias only affects ML systems that process data about people
C. Inappropriate bias is caused by overweighting of particular classes in algorithms
D. Inappropriate bias is caused by data used for training not being representative of the real world

Answer: A

Explanation:
The correct answer is D . The CT-AI syllabus explains that ML systems make decisions and predictions using algorithms and collected data , and that both can introduce bias into the results. Algorithmic bias can occur when the learning algorithm is incorrectly configured, for example where it overvalues some data compared with other data. Sample bias can occur when training data is not fully representative of the data space to which ML is applied. The syllabus further states that inappropriate bias is often caused by sample bias, but can also be caused by algorithmic bias.
Option A is too narrow because it describes only one algorithmic mechanism. Option B is also incomplete because it describes sample bias only. Option C is incorrect because the syllabus states that bias can be introduced into many types of AI-based systems, not only ML systems involving personal data. Therefore, the most complete and syllabus-aligned statement is that inappropriate bias can arise from either the algorithm or the data.
References/topics: CT-AI Syllabus Chapter 2, Section 2.4 "Bias."
=========

NEW QUESTION # 112
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

A. High complexity
B. Non-determinism
C. Self-learning
D. Robustness

Answer: D

Explanation:
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
* Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
* Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
* High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
* Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
:
ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.

NEW QUESTION # 113
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection. This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border. Which associated risk is most likely to occur when using this pre-trained model?

A. Inherited bias: the model could have inherited unknown defects
B. Insufficient function; the model was not trained to check for colors or words
C. There is no risk, as the model has already been trained
D. Improper data preparation

Answer: A

Explanation:
According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case.
Specifically, the syllabus states:
"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process."

NEW QUESTION # 114
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