PASS-SURE NEW CT-AI DUMPS PPT, CT-AI TEST ENGINE VERSION

Pass-Sure New CT-AI Dumps Ppt, CT-AI Test Engine Version

Pass-Sure New CT-AI Dumps Ppt, CT-AI Test Engine Version

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Tags: New CT-AI Dumps Ppt, CT-AI Test Engine Version, CT-AI New Test Materials, CT-AI Reliable Exam Syllabus, Latest CT-AI Practice Materials

If you want to pass the Certified Tester AI Testing Exam exam as shortly as possible, we will provide you the CT-AI exam dumps to help you to pass it. You only need to practice the Certified Tester AI Testing Exam exam dumps for adot 20 to 70 hours, you can pass it successfully. Our Certified Tester AI Testing Exam exam braindumps will save your time as well as improve your efficiency. Since the skilled professionals will guide you through you practice CT-AI the exam dumps.

ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 4
  • 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 5
  • 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.
Topic 6
  • 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.

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

NEW QUESTION # 16
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION

  • A. The quality of the labeling
  • B. The data pipeline
  • C. The number of classes
  • D. Biased data

Answer: C

Explanation:
* Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics.


NEW QUESTION # 17
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

  • A. Security issues
  • B. Bias issues
  • C. Accuracy issues
  • D. Privacy issues

Answer: C

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 # 18
Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large?
SELECT ONE OPTION

  • A. Using an Al-based tool to optimize the regression test suite by analyzing past test results
  • B. Identifying suitable tests by looking at the complexity of the test cases.
  • C. Using of a random subset of tests.
  • D. Automating test scripts using Al-based test automation tools.

Answer: A

Explanation:
A . Identifying suitable tests by looking at the complexity of the test cases.
While complexity analysis can help in selecting important test cases, it does not directly address the issue of optimizing the entire regression suite effectively.
B . Using a random subset of tests.
Randomly selecting test cases may miss critical tests and does not ensure an optimized regression suite. This approach lacks a systematic method for ensuring comprehensive coverage.
C . Automating test scripts using AI-based test automation tools.
Automation helps in running tests efficiently but does not inherently optimize the selection of tests to prevent the suite from growing too large.
D . Using an AI-based tool to optimize the regression test suite by analyzing past test results.
This is the most effective approach as AI-based tools can analyze historical test data, identify patterns, and prioritize tests that are more likely to catch defects based on past results. This method ensures an optimized and manageable regression test suite by focusing on the most impactful test cases.
Therefore, the correct answer is D because using an AI-based tool to analyze past test results is the best option to optimize regression test selection and manage the size of the regression suite effectively.


NEW QUESTION # 19
Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
SELECT ONE OPTION

  • A. Challenges in the creation of scenarios of human handover for autonomous systems.
  • B. The challenge of mimicking undefined scenarios generated due to self-learning
  • C. Challenges resulting from low accuracy of the models.
  • D. The challenge of providing explainability to the decisions made by the system.

Answer: A

Explanation:
AI test environments have several unique characteristics that differentiate them from traditional test environments. Let's evaluate each option:
A . Challenges resulting from low accuracy of the models.
Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.
B . The challenge of mimicking undefined scenarios generated due to self-learning.
AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.
C . The challenge of providing explainability to the decisions made by the system.
Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.
D . Challenges in the creation of scenarios of human handover for autonomous systems.
While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI .
Given the above points, option D is the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.


NEW QUESTION # 20
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. Reinforcement learning
  • B. Regression
  • C. Classification
  • D. Clustering

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 # 21
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