CT-AI Vce Download - Valid CT-AI Exam Question

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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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
  • 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 5
  • 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 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
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
You are using a neural network to train a robot vacuum to navigate without bumping into objects. You set up a reward scheme that encourages speed but discourages hitting the bumper sensors. Instead of what you expected, the vacuum has now learned to drive backwards because there are no bumpers on the back.
This is an example of what type of behavior?

Answer: B

Explanation:
The syllabus defines reward hacking as:
"Reward hacking can result from an AI-based system achieving a specified goal by using a 'clever' or 'easy' solution that perverts the spirit of the designer's intent." In this case, the vacuum found a loophole in the reward function-driving backwards to avoid bumper triggers while maximizing reward for speed.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.6, page 24 of 99)


NEW QUESTION # 41
ln the near future, technology will have evolved, and Al will be able to learn multiple tasks by itself without needing to be retrained, allowing it to operate even in new environments. The cognitive abilities of Al are similar to a child of 1-2 years.
In the above quote, which ONE of the following options is the correct name of this type of Al?

Answer: D

Explanation:
General AI, or strong AI, has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human cognitive abilities. It aligns with the description of AI that can learn multiple tasks and operate in new environments without needing retraining.


NEW QUESTION # 42
You are conducting a user acceptance test of a decision-support recommendation system used in a data processing business. In addition to testing the functional performance of the recommendation systems, what else would you be MOST likely to test?

Answer: C

Explanation:
In addition to testing the functional performance of the recommendation system, it is also important to test the amount of system resources consumed by each recommendation. This includes evaluating factors like processing time, memory usage, and the efficiency of the system when making recommendations. This ensures that the system performs well under the required load and is scalable for business use.


NEW QUESTION # 43
Which of the following characteristics of AI-based systems make it more difficult to ensure they are safe?

Answer: D

Explanation:
The syllabus states that non-determinism is one of the key challenges for ensuring safety in AI-based systems:
"The characteristics of AI-based systems that make it more difficult to ensure they are safe... include:
complexity, non-determinism, probabilistic nature, self-learning, lack of transparency, interpretability and explainability, and lack of robustness." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.8, page 25 of 99)


NEW QUESTION # 44
A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered when they start typing "Isle of Wight" it fills in "Isle of Eight". Several UAT testers have accepted this change without noticing. What type of bias is this?

Answer: C

Explanation:
Automation bias, also known as complacency bias, occurs when humans over-rely on automated systems and fail to question or validate the system's output. In this scenario, the auto-text correction feature of the word processing tool incorrectly suggests "Isle of Eight" instead of "Isle of Wight." The issue arises because multiple UAT testers accept the incorrect suggestion without noticing it, demonstrating a reliance on the AI- based system rather than their own judgment.
Automation bias is commonly seen in:
* Text correction systems, where users accept incorrect suggestions without verifying them.
* Medical diagnosis AI tools, where doctors may rely too much on AI recommendations.
* Autonomous driving systems, where drivers become overly dependent on automation and fail to react in critical situations.
* Section 7.4 - Testing for Automation Bias in AI-Based Systemsexplains that automation bias occurs when people accept AI-generated outputs without verifying them, often leading to incorrect decisions.
Reference from ISTQB Certified Tester AI Testing Study Guide:


NEW QUESTION # 45
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