CT-AI OFFICIAL CERT GUIDE, TEST CT-AI GUIDE

CT-AI Official Cert Guide, Test CT-AI Guide

CT-AI Official Cert Guide, Test CT-AI Guide

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Tags: CT-AI Official Cert Guide, Test CT-AI Guide, Latest Braindumps CT-AI Book, Latest CT-AI Exam Answers, CT-AI Valid Test Dumps

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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 5
  • 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 6
  • 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required thus reducing the time needed for each testing cycle.
How can an AI-based tool be expected to assist in this reduction?

  • A. By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases
  • B. By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category
  • C. By using A/B testing to compare the last update with the newest change and compare metrics between the two
  • D. By performing bayesian analysis to estimate the types of human interactions that are expected to be seen in the system and then selecting those test cases

Answer: A

Explanation:
AI-based tools can significantly optimize regression test suites by analyzing historical data, past test results, associated defects, and changes made to the software. These tools prioritize and select the most relevant test cases based on previous defect patterns and frequently failing features, which helps in reducing the test execution time while maintaining effectiveness.
The optimization process involves:
* Prioritizing test cases:AI-based tools rank test cases based on past defect detection trends, ensuring that the most relevant tests are executed first.
* Reducing redundant test cases:The tool can eliminate test cases that do not contribute significantly to defect detection, reducing overall test execution time.
* Augmenting test cases:The AI can also suggest new test cases if certain features are more prone to defects.
This approach has been proven to reduce regression test suite sizes by up to 50% while maintaining fault detection capabilities.
* Section 11.4 - Using AI for the Optimization of Regression Test Suitesstates that AI-based tools can optimize regression test suites by analyzing past test data and defect occurrences, leading to significant reductions in test execution time.
Reference from ISTQB Certified Tester AI Testing Study Guide:


NEW QUESTION # 69
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

  • A. Non-determinism
  • B. High complexity
  • C. Robustness
  • D. Self-learning

Answer: C

Explanation:
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
Reference:
ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.


NEW QUESTION # 70
In a conference on artificial intelligence (Al), a speaker made the statement, "The current implementation of Al using models which do NOT change by themselves is NOT true Al*. Based on your understanding of Al, is this above statement CORRECT or INCORRECT and why?
SELECT ONE OPTION

  • A. This statement is correct. In general, today the term Al is utilized incorrectly.
  • B. This statement is correct. In general, what is considered Al today may change over time.
  • C. This statement is incorrect. What is considered Al today will continue to be Al even as technology evolves and changes.
  • D. This statement is incorrect. Current Al is true Al and there is no reason to believe that this fact will change over time.

Answer: B

Explanation:
* A. This statement is incorrect. Current AI is true AI and there is no reason to believe that this fact will change over time.
AI is an evolving field, and the definition of what constitutes AI can change as technology advances.
* B. This statement is correct. In general, what is considered AI today may change over time.
The term AI is dynamic and has evolved over the years. What is considered AI today might be viewed as standard computing in the future. Historically, as technologies become mainstream, they often cease to be considered "AI".
* C. This statement is incorrect. What is considered AI today will continue to be AI even as technology evolves and changes.
This perspective does not account for the historical evolution of the definition of AI . As new technologies emerge, the boundaries of AI shift.
* D. This statement is correct. In general, today the term AI is utilized incorrectly.
While some may argue this, it is not a universal truth. The term AI encompasses a broad range of technologies and applications, and its usage is generally consistent with current technological capabilities.


NEW QUESTION # 71
A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered when they start typing "Isle of Wight" it fills in "Isle of Eight". Several UAT testers have accepted this change without noticing. What type of bias is this?

  • A. Complacency/Disregard
  • B. Geographical/Locality
  • C. Ignorance/Cognitive
  • D. Automation/Complacency

Answer: D

Explanation:
Automation bias, also known as complacency bias, occurs when humans over-rely on automated systems and fail to question or validate the system's output. In this scenario, the auto-text correction feature of the word processing tool incorrectly suggests "Isle of Eight" instead of "Isle of Wight." The issue arises because multiple UAT testers accept the incorrect suggestion without noticing it, demonstrating a reliance on the AI- based system rather than their own judgment.
Automation bias is commonly seen in:
* Text correction systems, where users accept incorrect suggestions without verifying them.
* Medical diagnosis AI tools, where doctors may rely too much on AI recommendations.
* Autonomous driving systems, where drivers become overly dependent on automation and fail to react in critical situations.
* Section 7.4 - Testing for Automation Bias in AI-Based Systemsexplains that automation bias occurs when people accept AI-generated outputs without verifying them, often leading to incorrect decisions.
Reference from ISTQB Certified Tester AI Testing Study Guide:


NEW QUESTION # 72
Which ONE of the following approaches to labelling requires the least time and effort?
SELECT ONE OPTION

  • A. Internal
  • B. Al-Assisted
  • C. Outsourced
  • D. Pre-labeled dataset

Answer: D

Explanation:
* Labelling Approaches: Among the options provided, pre-labeled datasets require the least time and effort because the data has already been labeled, eliminating the need for further manual or automated labeling efforts.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 4.5 Data Labelling for Supervised Learning, which discusses various approaches to data labeling, including pre-labeled datasets, and their associated time and effort requirements.


NEW QUESTION # 73
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