Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?
SELECT ONE OPTION

Correct Answer:A
* A. Using a pixel comparison of the GUI before and after the change to check the differences.
✑ Pixel comparison is a traditional method and does not involve AI. It compares
images at the pixel level, which can be effective but is not an intelligent approach. It is not considered an AI usage and is the least likely usage of AI for detecting GUI changes.
* B. Using computer vision to compare the GUI before and after the test object changes.
✑ Computer vision involves using AI techniques to interpret and process images. It is
a likely usage of AI for detecting changes in the GUI.
* C. Using vision-based detection of the GUI layout changes before and after test object changes.
✑ Vision-based detection is another AI technique where the layout and structure of
the GUI are analyzed to detect changes. This is a typical application of AI.
* D. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
✑ An ML-based classifier can intelligently determine significant changes and decide
if they need human review, which is a sophisticated AI application.

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

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

Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
SELECT ONE OPTION

Correct Answer:C
Coverage for Neuron Activation Values: Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.
Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 6.2 Coverage Measures for Neural Networks, which details different types of coverage measures, including sign change coverage.

Which ONE of the following options is an example that BEST describes a system with Al- based autonomous functions?
SELECT ONE OPTION

Correct Answer:D
AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.
Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems.

Upon testing a model used to detect rotten tomatoes, the following data was observed by the test engineer, based on certain number of tomato images.

For this confusion matrix which combinations of values of accuracy, recall, and specificity respectively is CORRECT?
SELECT ONE OPTION

Correct Answer:A
To calculate the accuracy, recall, and specificity from the confusion matrix provided, we use the following formulas:
✑ Confusion Matrix:
✑ Accuracy:
✑ Recall (Sensitivity):
✑ Specificity:
Therefore, the correct combinations of accuracy, recall, and specificity are 0.87, 0.9, and 0.84 respectively.
References:
✑ ISTQB CT-AI Syllabus, Section 5.1, Confusion Matrix, provides detailed formulas and explanations for calculating various metrics including accuracy, recall, and specificity.
✑ "ML Functional Performance Metrics" (ISTQB CT-AI Syllabus, Section 5).

Which ONE of the following approaches to labelling requires the least time and effort? SELECT ONE OPTION

Correct Answer:B
Labelling Approaches: Among the options provided, pre-labeled datasets require the least time and effort because the data has already been labeled, eliminating the need for further manual or automated labeling efforts.
Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 4.5 Data Labelling for Supervised Learning, which discusses various approaches to data labeling, including pre-labeled datasets, and their associated time and effort requirements.