CT-AI Exam Dumps : Certified Tester AI Testing Exam

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​​.

 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.

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​​.