Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION

1. A. Natural language processing on textual requirements
2. B. Analyzing source code for generating test cases
3. C. Machine learning on logs of execution
4. D. GUI analysis by computer vision

Correct Answer: A
When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:
✑ Natural Language Processing (NLP): NLP can analyze and understand human
language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.
✑ Why Not Other Options:
References: This aligns with the methodology discussed in the syllabus under the section on using AI for generating test cases from textual requirements.

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

1. A. Outsourced
2. B. Pre-labeled dataset
3. C. Internal
4. D. Al-Assisted

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.

"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real-
world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?

1. A. The difficulty of defining criteria for improvement before the model can be accepted.
2. B. The fast pace of change did not allow sufficient time for testing.
3. C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
4. D. There was an algorithmic bias in the Al system.

Correct Answer: A
✑ A. The difficulty of defining criteria for improvement before the model can be accepted.
✑ B. The fast pace of change did not allow sufficient time for testing.
✑ C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
✑ D. There was an algorithmic bias in the AI system.
Given the context of the self-learning nature and the need for real-time adaptability, option A is least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.

"Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.
Which ONE of the following combinations requires MAXIMIZATION? SELECT ONE OPTION

1. A. Maximize precision and accuracy
2. B. Maximize accuracy and recall
3. C. Maximize recall and precision
4. D. Maximize specificity number of classes

Correct Answer: C
✑ Prevalence Rate and Model Performance:
✑ Importance of Recall:
✑ Importance of Precision:
✑ Balancing Recall and Precision:
✑ Accuracy and Specificity:
✑ Conclusion:
: This explanation aligns with the principles outlined in the ISTQB CT-AI Syllabus, particularly sections on performance metrics for ML models and handling imbalanced datasets (Chapter 5: ML Functional Performance Metrics).

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

1. A. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
2. B. Train various models by changing the order of input features and verify that the R- Square score of these models vary significantly.
3. 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.
4. D. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

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.