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Quiz 2025 ISTQB CT-AI: Certified Tester AI Testing Exam Pass-Sure Reliable Exam Voucher

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?

A. Transparency
B. Autonomy
C. Explorability
D. Accessibility

Answer: A

Explanation:
The syllabus states:
"Transparency: This is considered to be the ease with which the algorithm and training data used to generate the model can be determined." Access to the training data is an example of transparency.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7, page 24 of 99)

NEW QUESTION # 36
Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I . Autonomy
II . Maintainability
III . Safety
IV . Transparency
V . Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION

A. Aspects I, IV, and V
B. Aspects II, III and IV
C. Aspects I, II, and III
D. Aspects III, IV, and V

Answer: D

Explanation:
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here's why:
Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.

NEW QUESTION # 37
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 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.
B. 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.
C. 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.
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: A

Explanation:
Metamorphic Testing (MT)is a testing technique that verifies AI-based systems by generatingfollow-up test casesbased on existing test cases. These follow-up test cases adhere to aMetamorphic 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, thesource test caseinvolves testing themedium-speed vehicle'stravel time.
* Thefollow-up test casesare derived byextrapolating travel times for fast and slow vehiclesusing 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
* Inroute optimization systems, metamorphic testing often applies transformations tospeed, distance, or conditionsto verify expected outcomes.
* (B) Decomposing each route into traffic density and vehicle power#
* While useful for statistical analysis, this approach does not generate follow-up test cases based on a definedmetamorphic relation (MR).
* (C) Selecting dissimilar routes and transforming them into a fast or slow route#
* Thisdoes not follow metamorphic testing principles, which require predictable transformations.
* (D) Running fast vehicles on long routes and slow vehicles on short routes#
* This methoddoes not maintain a controlled MRand introduces too manyuncontrolled variables.
* Metamorphic testing generates follow-up test cases based on a source test case."MT is a technique aimed at generating test cases which are based on a source test case that has passed.One or more follow- up test cases are generated by changing (metamorphizing) the source test case based on a metamorphic relation (MR)."
* MT has been used for testing route optimization AI systems."In the area of AI, MT has been used for testing image recognition, search engines, route optimization and voice recognition, among others." Why Option A is Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as it aligns with the principles ofmetamorphic testing by modifying input speeds and verifying expected results.

NEW QUESTION # 38
A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not.
The bank has enough data on past customers to randomly split the data into a training data set and a test
/validation data set. A logistic regression model is constructed on the training data set using the following independent variables:
Gender
Marital status
Number of dependents
Education
Income
Loan amount
Loan term
Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?

A. Experienced-based testing should be used to confirm that the training data set is operationally relevant.
This can include applying exploratory data analysis (EDA) to check for bias within the training data set.
B. A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model.
C. Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set, if the two models significantly differ, it will indicate there is bias in the original model.
D. Acceptance testing should be used to make sure the algorithm is suitable for the customer. The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation data set ensuring no bias is present.

Answer: A

Explanation:
Bias in an AI system occurs when the training data contains inherent prejudices that cause the model to make unfair predictions. Experience-based testing, particularlyExploratory Data Analysis (EDA), helps uncover these biases by analyzing patterns, distributions, and potential discriminatory factors in the training data.
* Option A:"Experience-based testing should be used to confirm that the training data set is operationally relevant. This can include applying exploratory data analysis (EDA) to check for bias within the training data set."
* This is the correct answer. EDA involves examining the dataset for bias, inconsistencies, or missing values, ensuring fairness in ML model predictions.
* Option B:"Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set. If the two models significantly differ, it will indicate there is bias in the original model."
* Back-to-back testing is used for regression testing and to compare versions of an AI system but is not primarily used to detect bias.
* Option C:"Acceptance testing should be used to make sure the algorithm is suitable for the customer.
The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation data set ensuring no bias is present."
* Acceptance testing focuses on meeting predefined business requirements rather than detecting and mitigating bias.
* Option D:"A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model."
* A/B testing is used for evaluating variations of a model rather than for explicitly identifying bias.
* Bias Testing Methods:"AI-based systems should be tested for algorithmic bias, sample bias, and inappropriate bias. Experience-based testing and EDA are useful for detecting bias".
* Exploratory Data Analysis (EDA):"EDA helps uncover potential bias in training data through visualization and statistical analysis".
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,Option A is the best choice for detecting bias in the loan applicant model.

NEW QUESTION # 39
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. Improper data preparation
B. There is no risk, as the model has already been trained
C. Insufficient function; the model was not trained to check for colors or words
D. Inherited bias: the model could have inherited unknown defects

Answer: D

Explanation:
A major risk when using apre-trained neural networkfor logo detection is that it mayinherit biases and defectsfrom the original dataset and training process. This means that the model could misidentify or fail to recognize certain logos due to:
* Differences in data preparation:The original training data may have used a different preprocessing method than the new dataset, leading to inconsistencies.
* Limited transparency:The exact details of the dataset and biases within it may not be known, which can cause unexpected behavior.
* Bias in logo detection:If the model was trained on a dataset with certain color or text preferences, it may disproportionately misidentify logos with similar characteristics.
This inherited bias can result in:
* False Positives:Recognizing other brand logos as the beer company's logo.
* False Negatives:Failing to detect the actual logo when variations occur (e.g., different lighting or partial visibility).
* Algorithmic Bias:The model may favor certain shapes or color contrasts due to biased training data.
Thus,the most appropriate risk associated with using this pre-trained model is inherited bias.
* Section 1.8.3 - Risks of Using Pre-Trained Models and Transfer Learningexplains how pre-trained models may inheritbiases and undocumented defectsthat affect performance in a new environment.
Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 40
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