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ISTQB Certified Tester AI Testing Exam Sample Questions (Q90-Q95):
NEW QUESTION # 90
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 # 91
Which of the following statements about explainable AI is correct?
- A. Explainability refers to how easily the algorithms and training data needed to create the model can be determined
- B. According to The Royal Society, one reason for explainable AI is to increase user confidence in the system
- C. According to The Royal Society, one reason for explainable AI is to eliminate the need for risk and vulnerability assessments
- D. Interpretability refers to how easily users can determine whether the result provided by the AI- based system is correct
Answer: B
Explanation:
Section2.10 - Explainability and Transparency of the ISTQB CT-AI syllabus describes explain able AI as the ability of a system to provide human-understandable insight into its decisions. The syllabus references The Royal Society's reportas a foundational source explaining why explainability is important. Among the stated motivations is the need toincrease user trust and confidencein AI systems by making their decisions understandable and justifiable. Therefore, Option C directly reflects the syllabus content .
NEW QUESTION # 92
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION
- A. Check the input test data for potential sample bias.
- B. Testing the data pipeline for any sources for algorithmic bias.
- C. Testing the distribution shift in the training data for inappropriate bias.
- D. Test the model during model evaluation for data bias.
Answer: D
Explanation:
Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.
* Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.
* Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
* Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.
* Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.
Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias.
References:
* ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.
* Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.
NEW QUESTION # 93
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?
- A. Equivalence partitioning
- B. State transition testing
- C. Static analysis
- D. Exploratory testing
Answer: D
NEW QUESTION # 94
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 size of the application is consuming too much of the phone's storage capacity.
- B. The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle.
- C. The feedback requires a physical connection and cannot be sent over the Internet.
- D. Mobile operating systems cannot process machine learning algorithms.
Answer: B
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."
NEW QUESTION # 95
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