LATEST CT-AI EXAM ANSWERS, CT-AI INTEREACTIVE TESTING ENGINE

Latest CT-AI Exam Answers, CT-AI Intereactive Testing Engine

Latest CT-AI Exam Answers, CT-AI Intereactive Testing Engine

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Tags: Latest CT-AI Exam Answers, CT-AI Intereactive Testing Engine, CT-AI Latest Test Pdf, CT-AI Valid Exam Vce, Valid CT-AI Test Questions

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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • systems from those required for conventional systems.
Topic 2
  • 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 3
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
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
  • 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 6
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

>> Latest CT-AI Exam Answers <<

Free PDF Quiz 2025 High Pass-Rate ISTQB Latest CT-AI Exam Answers

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q18-Q23):

NEW QUESTION # 18
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

  • A. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data.
  • B. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data.
  • C. Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate.
  • D. Run the test several times to ensure that the AI always returns the same correct test result.

Answer: C

Explanation:
Probabilistic and non-deterministic AI-based systemsdo not always produce the same output for identical inputs. This makes traditional testing approaches ineffective. Instead, the best approach is torun tests multiple times and analyze results statistically.
* Statistical Validity:Running tests multiple times ensures that observed results are statistically significant. Instead of relying on a single test run,analyzing multiple iterations helps determine trends, probabilities, and outliers.
* Expected Result Tolerance:AI-based systems may produce different results within an acceptable range. Defining acceptable tolerances (e.g., "result must be within 2% of the optimal value") improves test effectiveness.
* A (Run Several Times for the Same Correct Result):AI systems are ofteninherently non- deterministicand may not return the exact same result every time. Expecting identical outputs contradicts the nature of these systems.
* B & C (Decomposing Tests into Data Ingestion Tests):While data ingestion quality is important, it does notdirectlysolve the issue of probabilistic test results. Statistical analysis is the key approach.
* ISTQB CT-AI Syllabus (Section 8.4: Challenges Testing Probabilistic and Non-Deterministic AI- Based Systems)
* "For probabilistic systems, running a test multiple times may be necessary to obtain a statistically valid test result.".
* "Where a single definitive output is not possible, results should be analyzed statistically rather than relying on individual test cases.".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sinceprobabilistic AI systems do not always return the same result, the best approach is torun multiple test iterations and validate results statistically. Hence, thecorrect answer is D.


NEW QUESTION # 19
Which ONE of the following approaches to labelling requires the least time and effort?
SELECT ONE OPTION

  • A. Al-Assisted
  • B. Internal
  • 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 # 20
Which of the following is a technique used in machine learning?

  • A. Boundary value analysis
  • B. Decision tables
  • C. Equivalence partitioning
  • D. Decision trees

Answer: D

Explanation:
Decision trees are a foundational algorithm used in supervised machine learning. The syllabus describes:
"A decision tree is a tree-like ML model whose nodes represent decisions and whose branches represent possible outcomes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.4)


NEW QUESTION # 21
An airline has created an 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?

  • A. The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated
  • B. The model suffers from a lack of transparency and therefore should be regularly tested to ensure that any progressive errors are detected soon enough for the problem to be mitigated
  • C. The model suffers from drift and therefore the performance standard should be eased until a new model with more transparency can be developed
  • D. The model suffers from corruption and therefore should be reloaded into the computer system being used, preferably with a method of version control to prevent further changes

Answer: A

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." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6, Page 54 of 99)


NEW QUESTION # 22
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop-off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail. The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?

  • A. The robot must complete 99.99% of its deliveries each day
  • B. The robot must recharge for no more than six hours a day
  • C. The robot must evolve to optimize its routing
  • D. The robot must record the time of each delivery which is compiled into a report

Answer: C

Explanation:
In the syllabus, theevolutioncharacteristic for AI-based systems means the ability of the system to evolve and adapt its behavior in response to changes in the environment or in its own performance:
"Evolution is the system's ability to change itself to adapt to new situations, different hardware, or a changing operational environment." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.3)


NEW QUESTION # 23
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