TEST CT-AI CENTRES & CT-AI RELIABLE PRACTICE QUESTIONS

Test CT-AI Centres & CT-AI Reliable Practice Questions

Test CT-AI Centres & CT-AI Reliable Practice Questions

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100% Pass Quiz 2025 ISTQB CT-AI: Certified Tester AI Testing Exam Updated Test Centres

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

  • A. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data
  • B. Run the test several times to ensure that the AI always returns the same correct test result
  • C. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data
  • D. Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate

Answer: D

Explanation:
The syllabus states:
"When testing probabilistic and non-deterministic systems, the same input may produce different outputs.
Tests need to be run several times to produce statistically valid test results, ensuring that an appropriate number of answers are accurate." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.4, page 58 of 99)


NEW QUESTION # 52
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?

  • A. Reward-hacking
  • B. Error-shortcircuiting
  • C. Transparency
  • D. Interpretability

Answer: A

Explanation:
Reward hacking occurs when an AI-based system optimizes for a reward function in a way that is unintended by its designers, leading to behavior that technically maximizes the defined reward but does not align with the intended objectives.
In this case, the robot vacuum was given a reward scheme that encouraged speed while discouraging collisions detected by bumper sensors. However, since the bumper sensors were only on the front, the AI found a loophole-driving backward-thereby avoiding triggering the bumper sensors while still maximizing its reward function.
This is a classic example of reward hacking, where an AI "games" the system to achieve high rewards in an unintended way. Other examples include:
* An AI playing a video game that modifies the score directly instead of completing objectives.
* A self-learning system exploiting minor inconsistencies in training data rather than genuinely improving performance.
* Section 2.6 - Side Effects and Reward Hackingexplains that AI systems may produce unexpected, and sometimes harmful, results when optimizing for a given goal in ways not intended by designers.
* Definition of Reward Hacking in AI: "The activity performed by an intelligent agent to maximize its reward function to the detriment of meeting the original objective" Reference from ISTQB Certified Tester AI Testing Study Guide:


NEW QUESTION # 53
Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem?
SELECT ONE OPTION

  • A. Security issues
  • B. Accuracy issues
  • C. Bias issues
  • D. Privacy issues

Answer: B

Explanation:
The question refers to a problem where data used for an object detection ML system was labelled incorrectly.
This issue is most closely related to "accuracy issues." Here's a detailed explanation:
* Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly impacts the model's accuracy, leading to poor performance because the model learns incorrect patterns.
* Why Not Other Options:
* Security Issues: This pertains to data breaches or unauthorized access, which is not relevant to the problem of incorrect data labeling.
* Privacy Issues: This concerns the protection of personal data and is not related to the accuracy of data labeling.
* Bias Issues: While bias in data can affect model performance, it specifically refers to systematic errors or prejudices in the data rather than outright incorrect labeling.
References:This explanation is consistent with the concepts covered in the ISTQB CT-AI syllabus under dataset quality issues and their impact on machine learning models.


NEW QUESTION # 54
Which ONE of the following hardware is MOST suitable for implementing Al when using ML?
SELECT ONE OPTION

  • A. High powered CPUs.
  • B. 64-bit CPUs.
  • C. Hardware supporting fast matrix multiplication.
  • D. Hardware supporting high precision floating point operations.

Answer: C

Explanation:
A . 64-bit CPUs.
While 64-bit CPUs are essential for handling large amounts of memory and performing complex computations, they are not specifically optimized for the types of operations commonly used in machine learning.
B . Hardware supporting fast matrix multiplication.
Matrix multiplication is a fundamental operation in many machine learning algorithms, especially in neural networks and deep learning. Hardware optimized for fast matrix multiplication, such as GPUs (Graphics Processing Units), is most suitable for implementing AI and ML because it can handle the parallel processing required for these operations efficiently.
C . High powered CPUs.
High powered CPUs are beneficial for general-purpose computing tasks and some aspects of ML, but they are not as efficient as specialized hardware like GPUs for matrix multiplication and other ML-specific tasks.
D . Hardware supporting high precision floating point operations.
High precision floating point operations are important for scientific computing and some specific AI tasks, but for many ML applications, fast matrix multiplication is more critical than high precision alone.
Therefore, the correct answer is B because hardware supporting fast matrix multiplication, such as GPUs, is most suitable for the parallel processing requirements of machine learning.


NEW QUESTION # 55
Which of the following problems would best be solved using the supervised learning category of regression?

  • A. 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.
  • B. Recognizing a knife in carry on luggage at a security checkpoint in an airport scanner.
  • C. Determining if an animal is a pig or a cow based on image recognition.
  • D. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.

Answer: A

Explanation:
Understanding Supervised Learning - RegressionSupervised learning is a category of machine learning where the model is trained on labeled data. Within this category,regressionis used when the goal is to predict a continuous numeric value.
* Regressiondeals with problems where the output variable is continuous in nature, meaning it can take any numerical value within a range.
* Common examples include predicting prices, estimating demand, and analyzing production trends.
* (A) 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.#(Correct)
* This is a classicregression problembecause it involves predicting a continuous variable:daily egg productionbased on the input variablechicken's age.
* The goal is to find a numerical relationship between age and egg production, which makesregression the appropriate supervised learning method.
* (B) Recognizing a knife in carry-on luggage at a security checkpoint in an airport scanner.#(Incorrect)
* This is animage recognition task, which falls underclassification, not regression.
* Classification problems involve assigning inputs to discrete categories (e.g., "knife detected" or
"no knife detected").
* (C) Determining if an animal is a pig or a cow based on image recognition.#(Incorrect)
* This is anotherclassification problemwhere the goal is to categorize an image into one of two labels (pig or cow).
* (D) Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.#(Incorrect)
* This problem could involve a mix ofclassificationandassociation rule learning, but it does not explicitly predict a continuous variable in the way regression does.
* Regression is used when predicting a numeric output."Predicting the age of a person based on input data about their habits or predicting the future prices of stocks are examples of problems that use regression."
* Supervised learning problems are divided into classification and regression."If the output is numeric and continuous in nature, it may be regression."
* Regression is commonly used for predicting numerical trends over time."Regression models result in a numerical or continuous output value for a given input." Analysis of Answer ChoicesReferences from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as it aligns with the principles of regression-based supervised learning.


NEW QUESTION # 56
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