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CT-AI Actual Collection: Certified Tester AI Testing Exam - CT-AI Quiz Braindumps & CT-AI Exam Guide

In order to ensure the quality of our CT-AI preparation materials, we specially invited experienced team of experts to write them. The content of our CT-AI practice engine comes from a careful analysis and summary of previous exam syllabus, so that you can accurately grasp the core test sites. At the same time, our proffesional experts are keeping a close eye on the changes of the exam questions and answers. So that our CT-AI Study Guide can be the latest and most accurate.

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

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 3

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 4

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 5

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

Topic 6

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 7

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 8

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 9

Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q76-Q81):

NEW QUESTION # 76
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. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
B. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
C. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
D. 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.

Answer: D

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, optionCis 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 # 77
A startup company has implemented a new facial recognition system for a banking application for mobile devices. The application is intended to learn at run-time on the device to determine if the user should be granted access. It also sends feedback over the Internet to the application developers. The application deployment resulted in continuous restarts of the mobile devices.
Which of the following is the most likely cause of the failure?

A. The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle
B. Mobile operating systems cannot process machine learning algorithms
C. The feedback requires a physical connection and cannot be sent over the Internet
D. The size of the application is consuming too much of the phone's storage capacity

Answer: A

Explanation:
The syllabus highlights that on-device training and processing require considerable computational power, which may exceed the capabilities of some mobile devices:
"Self-learning and continuous learning systems require large amounts of computational power, which can impact system performance and stability if the hardware is not powerful enough." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.3, page 22 of 99)

NEW QUESTION # 78
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. Privacy issues
B. Security issues
C. Accuracy issues
D. Bias issues

Answer: C

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 # 79
An airline has created a ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in the load weights became less accurate. After some exploratory data analysis it became apparent that luggage weights were higher in the winter than in summer.
Which of the following statements BEST describes the problem and how it could have been prevented?

A. The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated.
B. The model suffers from drift and therefore the performance standard should be eased until a newmodel with more transparency can be developed.
C. The model suffers from a lack of transparency and therefore should be regularly tested to ensure that any progressive errors are detected soon enough for the problem to be mitigated.
D. The model suffers from corruption and therefore should be reloaded into the computer system being used, preferably with a method of version control to prevent further changes.

Answer: A

Explanation:
The problem described in the question is a classic case ofconcept drift. Concept drift occurs when the relationship between input variables and the output variable changes over time, leading to a decline in model accuracy.
In this scenario, theaverage passenger and baggage weightsused in the model changed due to seasonal variations, but the model was not updated accordingly. This resulted in inaccurate predictions for fuel requirements in the winter season. This is an example ofseasonal drift, where model behavior changes periodically due to recurring trends (e.g., higher luggage weights in winter compared to summer).
To prevent such problems:
* Themodel should be regularly testedfor concept drift against agreed ML functional performance criteria.
* Exploratory Data Analysis (EDA)should be performed periodically to detect gradual changes in input distributions.
* Retraining of the modelwith updated training data should be done to maintain accuracy.
* If drift is detected, mitigation techniques such asincremental learning, retraining with new data, or adjusting model parametersshould be employed.
* Option B (Easing the performance standard instead of addressing drift): Lowering the performance standard is not a solution; it only masks the problem without fixing it. Instead, regular testing and retraining should be used to handle drift properly.
* Option C (Corruption and reloading the model): Model corruption is unrelated to this issue.
Corruption refers to accidental or malicious damage to the model or data, whereas this case is due to a changing data environment.
* Option D (Lack of transparency): Transparency refers to how understandable the model's decisions are, but the problem here is a change in data distributions, making drift the primary concern.
* ISTQB CT-AI Syllabus (Section 7.6: Testing for Concept Drift)
* "The operational environment can change over time without the trained model changing correspondingly. This phenomenon is known as concept drift and typically causes the outputs of the model to become increasingly less accurate and less useful."
* "Systems that may be prone to concept drift should be regularly tested against their agreed ML functional performance criteria to ensure that any occurrences of concept drift are detected soon enough for the problem to be mitigated."
* ISTQB CT-AI Syllabus (Section 7.7: Selecting a Test Approach for an ML System)
* "If concept drift is detected, it may be mitigated by retraining the system with up-to-date training data followed by confirmation testing, regression testing, and possibly A/B testing where the updated system must outperform the original system." Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the question describes a situation whereseasonal variations affected input data distributions, the correct answer isA: The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated.

NEW QUESTION # 80
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determinedthat there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?

A. The number of parameters to test can be reduced to less than a dozen.
B. All high priority defects will be identified using this method.
C. Pairwise cannot be applied to this problem because there is AI involved and the evolving values may result in unexpected results that cannot be verified.
D. While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them.

Answer: D

Explanation:
Pairwise testing is a combinatorial testing technique that reduces the number of test cases by focusing on testing interactions between pairs of parameters rather than all possible combinations. It is widely used in AI- based systems, including autonomous vehicles, where the number of possible input parameter combinations can be extremely high.
* Option A:"The number of parameters to test can be reduced to less than a dozen."
* This is incorrect. While pairwise testing significantly reduces the number of test cases, it does not necessarily limit them to a fixed number like a dozen. The final number of tests depends on the number of parameters and their possible values.
* Option B:"All high priority defects will be identified using this method."
* This is incorrect. While pairwise testing is effective in detecting defects caused by interactions between two parameters, it may not uncover defects resulting from more complex interactions involving three or more parameters.
* Option C:"While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them."
* This is the correct answer. Even though pairwise testing reduces the number of test cases, AI- based systems such as autonomous vehicles still have a large number of test scenarios. Therefore, automation is often necessary to execute all test cases within the available time.
* Option D:"Pairwise cannot be applied to this problem because there is AI involved, and the evolving values may result in unexpected results that cannot be verified."
* This is incorrect. Pairwise testing can still be applied to AI-based systems, including those that evolve over time. However, additional testing techniques may be required to verify evolving behavior.
* Pairwise Testing for AI Systems:"Pairwise testing is widely used because it effectively reduces the number of test cases while maintaining defect detection capability".
* Automation Requirement:"In practice, even with pairwise testing, extensive test suites may still require automation".
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:

NEW QUESTION # 81
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