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ISTQB Certified Tester AI Testing Exam Sample Questions (Q54-Q59):

NEW QUESTION # 54
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION

A. Case control structures
B. Search engines
C. Procedural programming
D. Genetic algorithms

Answer: D

Explanation:
* Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI . They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI.

NEW QUESTION # 55
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION

A. A comparison of the performance of two different ML implementations on the same input data.
B. A comparison of the performance of an ML system on two different input datasets.
C. A comparison of two different websites for the same company to observe from a user acceptance perspective.
D. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.

Answer: B

Explanation:
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
* Understanding A/B Testing:
* In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
* Application in Machine Learning:
* In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
* Why Option C is the Least Descriptive:
* Option C describes comparing the performance of an ML system on two different input datasets.
This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
* Clarifying the Other Options:
* A. A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
* B. A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
* D. A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
References:
* ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
* "Understanding A/B Testing" (ISTQB CT-AI Syllabus).

NEW QUESTION # 56
A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION

A. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
B. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
C. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
D. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

Answer: A

Explanation:
A . Add 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
Adding more data to the training set can affect the R-Square score, but it does not directly verify the correctness of the implementation.
B . Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
Changing the order of input features should not significantly affect the R-Square score if the implementation is correct, but this approach is more about testing model robustness rather than correctness of the implementation.
C . Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
This approach directly compares the performance of two implementations of the same algorithm. If both implementations produce similar R-Square scores on the same training and testing data, it suggests that the new implementation "X" is correct.
D . Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
Dropping data can lead to variations in the R-Square score but does not directly verify the correctness of the implementation.
Therefore, option C is the most appropriate strategy because it directly compares the performance of the new implementation "X" with another implementation using the same algorithm and datasets, which helps in verifying the correctness of the implementation.

NEW QUESTION # 57
Which of the following is one of the reasons for data mislabelling?

A. Lack of domain knowledge
B. Interoperability error
C. Expert knowledge
D. Small datasets

Answer: A

Explanation:
The syllabus lists multiple reasons for mislabelled data, including the lack of domain knowledge:
"Lack of required domain knowledge may lead to incorrect labelling."
(Reference: ISTQB CT-AI Syllabus v1.0, Section 4.5.2, page 38 of 99)

NEW QUESTION # 58
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 analyze mathematical models and is given a set of landscape pictures to classify
B. 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
C. It has been trained to analyze customer buying trend data and is given information on supplier cost data
D. It has been trained to recognize cats and is given an image of a dog

Answer: B

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 # 59
......

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Quiz 2025 ISTQB Efficient CT-AI: Certified Tester AI Testing Exam Actualtest

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q54-Q59):

Quiz High Pass-Rate CT-AI - Certified Tester AI Testing Exam Actualtest

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