Which ONE of the following options BEST DESCRIBES clustering? SELECT ONE OPTION
Correct Answer:C
Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:
✑ A. Clustering is classification of a continuous quantity.
✑ B. Clustering is supervised learning.
✑ C. Clustering is done without prior knowledge of output classes.
✑ D. Clustering requires you to know the classes.
Therefore, the correct answer is C because clustering is an unsupervised learning technique done without prior knowledge of output classes.
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION
Correct Answer:D
✑ A. Black box attacks based on adversarial examples create an exact duplicate model of the original.
✑ B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
✑ C. These attacks can't be prevented by retraining the model with these examples augmented to the training data.
✑ D. These examples are model specific and are not likely to cause another model trained on the same task to fail.
Therefore, the correct answer is D because adversarial examples are typically model- specific and may not cause another model trained on the same task to fail.
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
Correct Answer:B
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:
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.
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer). A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III): I.Pairwise testing of combinations
II.Testing each individual model for accuracy III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION
Correct Answer:B
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
✑ Pairwise testing of combinations (I): This method is useful for testing interactions
between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
✑ Testing each individual model for accuracy (II): Ensuring that each model in the
workflow performs accurately on its own is crucial before integrating them into a combined workflow.
✑ A/B testing of different sequences of models (III): This involves comparing different
sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
References:
✑ ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.
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
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.
Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
Correct Answer:C
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here??s why:
✑ Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to
patients.
✑ Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
✑ Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
✑ Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.
References: This explanation is aligned with the critical quality characteristics for AI-based systems as mentioned in the ISTQB CT-AI syllabus, focusing on the certification of medical devices.