Valid Dumps CT-AI Ppt - New CT-AI Test Answers

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On the basis of the current social background and development prospect, the CT-AI certifications have gradually become accepted prerequisites to stand out the most in the workplace. As far as we know, in the advanced development of electronic technology, lifelong learning has become more accessible, which means everyone has opportunities to achieve their own value and life dream. Our CT-AI Exam Materials are pleased to serve you as such an exam tool. You will have a better future with our CT-AI study braindumps!

ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 2	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 3	Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 4	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Topic 5	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 6	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 7	systems from those required for conventional systems.
Topic 8	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 9	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 10	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 11	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

A. Clustering
B. Reinforcement learning
C. Classification
D. Regression

Answer: C

Explanation:
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.

NEW QUESTION # 17
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).
I . Autonomy
II . Maintainability
III . Safety
IV . Transparency
V . Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION

A. Aspects I, IV, and V
B. Aspects I, II, and III
C. Aspects III, IV, and V
D. Aspects II, III and IV

Answer: C

Explanation:
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.

NEW QUESTION # 18
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION

A. Self-learning systems are expected to deal with new situations without explicitly having to program for it.
B. Al systems are inherently flexible.
C. Al systems require changing of operational environments; therefore, flexibility is required.
D. Flexible Al systems allow for easier modification of the system as a whole.

Answer: D

Explanation:
Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.
AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.
AI systems require changing operational environments; therefore, flexibility is required (B): While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.
Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.
Self-learning systems are expected to deal with new situations without explicitly having to program for it (D): This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.
Hence, the correct answer is C. Flexible AI systems allow for easier modification of the system as a whole.
Reference:
ISTQB CT-AI Syllabus Section 2.1 on Flexibility and Adaptability discusses the importance of flexibility in AI systems and how it enables easier modification and adaptability to new situations.
Sample Exam Questions document, Question #30 highlights the importance of flexibility in AI systems.

NEW QUESTION # 19
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION

A. Testing the distribution shift in the training data for inappropriate bias.
B. Testing the data pipeline for any sources for algorithmic bias.
C. Check the input test data for potential sample bias.
D. Test the model during model evaluation for data bias.

Answer: D

Explanation:
Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.
Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.
Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.
Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.
Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline is B. Test the model during model evaluation for data bias.
Reference:
ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.
Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.

NEW QUESTION # 20
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

A. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
B. Using a pixel comparison of the GUI before and after the change to check the differences.
C. Using a computer vision to compare the GUI before and after the test object changes.
D. Using a vision-based detection of the GUI layout changes before and after test object changes.

Answer: B

Explanation:
* 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.

NEW QUESTION # 21
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You will need to pass the Certified Tester AI Testing Exam (CT-AI) exam to achieve the Certified Tester AI Testing Exam (CT-AI) certification. Due to extremely high competition, passing the ISTQB CT-AI exam is not easy; however, possible. You can use Exam4Free products to pass the CT-AI Exam on the first attempt. The Certified Tester AI Testing Exam (CT-AI) practice exam gives you confidence and helps you understand the criteria of the testing authority and pass the ISTQB CT-AI exam on the first attempt.

New CT-AI Test Answers: https://www.exam4free.com/CT-AI-valid-dumps.html

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VALID DUMPS CT-AI PPT - NEW CT-AI TEST ANSWERS

Valid Dumps CT-AI Ppt - New CT-AI Test Answers