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| Section | Objectives |
|---|---|
| Introduction to AI Testing | - AI systems overview
|
| Testing AI-Based Systems | - Test levels for AI systems
|
| AI System Lifecycle and Operations | - Deployment and monitoring
|
| Data Quality and Bias | - Data quality assurance
|
| AI Quality Characteristics | - Quality attributes
|
| Machine Learning Fundamentals for Testing | - Model types
|
| Ethics and Risk in AI Testing | - Risk-based testing for AI
|
The CT-AI real questions are written and approved by our It experts, and tested by our senior professionals with many years' experience. The content of our CT-AI pass guide covers the most of questions in the actual test and all you need to do is review our CT-AI VCE Dumps carefully before taking the exam. Then you can pass the actual test quickly and get certification easily.
NEW QUESTION # 74
Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?
Answer: C
Explanation:
AI systems, particularly machine learning models, need to exhibit adaptability and flexibility to handle slight variations in input data without requiring retraining. The ISTQB CT-AI syllabus outlines adaptability as a crucial feature of AI systems, especially when the system is exposed to variations in its operational environment.
* Option A:"It has been trained to recognize cats and is given an image of a dog."
* This scenario introduces an entirely new class (dogs), which is outside the AI system's expected scope. If the AI was only trained to recognize cats, it would not be expected to recognize dogs correctly without retraining. This does not demonstrate adaptability as expected from an AI system.
* Option B:"It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution."
* This is an example of an AI system encountering a variation of its training data rather than entirely new data. Most AI-based image processing models can adapt to different resolutions by applying downsampling or other pre-processing techniques. Since the data remains within the domain of human faces, the model should be able to process the higher-resolution image without significant issues.
* Option C:"It has been trained to analyze mathematical models and is given a set of landscape pictures to classify."
* This represents a complete shift in the data type from structured numerical data to unstructured image data. The AI system is unlikely to adapt effectively, as it has not been trained on image classification tasks.
* Option D:"It has been trained to analyze customer buying trend data and is given information on supplier cost data."
* This introduces a significant domain shift. Customer buying trends focus on consumer behavior, while supplier cost data relates to pricing structures and logistics. The AI system would likely require retraining to process the new data meaningfully.
* Adaptability Requirements:The syllabus discusses that AI-based systems must be able to adapt to changes in their operational environment and constraints, including minor variations in input quality (such as resolution changes).
* Autonomous Learning & Evolution:AI systems are expected to improve and handle evolving inputs based on prior experience.
* Challenges in Testing Self-Learning Systems:AI systems should be tested to ensure they function correctly when encountering new but related data, such as different resolutions of the same object.
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,option Bis the best choice as it aligns with the adaptability characteristics expected from AI-based systems.
NEW QUESTION # 75
Which of the following is a dataset issue that can be resolved using pre-processing?
Answer: B
Explanation:
The syllabus describes that data pre-processing includes cleaning (e.g., fixing or removing invalid data) and transforming data (e.g., changing data types such as numbers stored as strings).
"Transformation: The format of the given data is changed... converting categorical data into numerical data, changing image formats..."
NEW QUESTION # 76
An airline has created a ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in the load weights became less accurate. After some exploratory data analysis it became apparent that luggage weights were higher in the winter than in summer.
Which of the following statements BEST describes the problem and how it could have been prevented?
Answer: B
Explanation:
The syllabus states:
"Concept drift occurs when the operational environment changes without the trained model changing correspondingly. The outputs of the model become less accurate and less useful.
Therefore, the operational model should be regularly evaluated against its acceptance criteria."
NEW QUESTION # 77
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
Answer: D
Explanation:
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:
Security Issues: This pertains to data breaches or unauthorized access, which is not relevant to the problem of incorrect data labeling.
Privacy Issues: This concerns the protection of personal data and is not related to the accuracy of data labeling.
Bias Issues: While bias in data can affect model performance, it specifically refers to systematic errors or prejudices in the data rather than outright incorrect labeling.
NEW QUESTION # 78
Which of the following statements about bias in AI-based systems is MOST correct?
Answer: D
Explanation:
Inappropriate bias in AI systems is typically caused by the data used for training not being representative of the real world. This can lead to the model making biased or incorrect predictions when applied to real-world scenarios. While algorithmic factors can also contribute to bias, the primary issue arises from biased or unrepresentative training data.
NEW QUESTION # 79
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