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ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large?
SELECT ONE OPTION

A. Using an Al-based tool to optimize the regression test suite by analyzing past test results
B. Automating test scripts using Al-based test automation tools.
C. Using of a random subset of tests.
D. Identifying suitable tests by looking at the complexity of the test cases.

Answer: A

Explanation:
* A. Identifying suitable tests by looking at the complexity of the test cases.
* While complexity analysis can help in selecting important test cases, it does not directly address the issue of optimizing the entire regression suite effectively.
* B. Using a random subset of tests.
* Randomly selecting test cases may miss critical tests and does not ensure an optimized regression suite. This approach lacks a systematic method for ensuring comprehensive coverage.
* C. Automating test scripts using AI-based test automation tools.
* Automation helps in running tests efficiently but does not inherently optimize the selection of tests to prevent the suite from growing too large.
* D. Using an AI-based tool to optimize the regression test suite by analyzing past test results.
* This is the most effective approach as AI-based tools can analyze historical test data, identify patterns, and prioritize tests that are more likely to catch defects based onpast results. This method ensures an optimized and manageable regression test suite by focusing on the most impactful test cases.
Therefore, the correct answer isDbecause using an AI-based tool to analyze past test results is the best option to optimize regression test selection and manage the size of the regression suite effectively.

NEW QUESTION # 14
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. Accuracy issues
B. Security issues
C. Privacy issues
D. Bias issues

Answer: A

NEW QUESTION # 15
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

A. Reinforcement learning
B. Clustering
C. Regression
D. Classification

Answer: D

Explanation:
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification.
Here's a breakdown:
* Classification: This type of machine learning involves categorizing input data into predefined classes.
In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
* Why Not Other Options:
* Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
* Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
* Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.
References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.

NEW QUESTION # 16
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. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
B. 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.
C. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
D. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

Answer: B

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 # 17
Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
SELECT ONE OPTION

A. Training data - validation data
B. Training data * test data
C. Training data - validation data - test data
D. Validation data - test data

Answer: C

Explanation:
The process of developing a machine learning model typically involves the use of three types of datasets:
* Training Data:This is used to train the model, i.e., to learn the patterns and relationships in the data.
* Validation Data:This is used to tune the model's hyperparameters and to prevent overfitting during the training process.
* Test Data:This is used to evaluate the final model's performance and to estimate how it will perform on unseen data.
Let's analyze each option:
* A. Training data - validation data - test data
* This option correctly includes all three types of datasets used in the process of creating and validating a model. The training data is used for learning, validation data for tuning, and test data for final evaluation.
* B. Training data - validation data
* This option misses the test data, which is crucial for evaluating the model's performance on unseen data after the training and validation phases.
* C. Training data - test data
* This option misses the validation data, which is important for tuning the model and preventing overfitting during training.
* D. Validation data - test data
* This option misses the training data, which is essential for the initial learning phase of the model.
Therefore, the correct answer isAbecause it includes all necessary datasets used during the process of learning and creating the model: training, validation, and test data.

NEW QUESTION # 18
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