CT-AI VALID TEST TEST - VCE CT-AI EXAM SIMULATOR

CT-AI Valid Test Test - VCE CT-AI Exam Simulator

CT-AI Valid Test Test - VCE CT-AI Exam Simulator

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Tags: CT-AI Valid Test Test, VCE CT-AI Exam Simulator, Sure CT-AI Pass, Latest Test CT-AI Discount, Cost Effective CT-AI Dumps

If you want to sharpen your skills, or get the CT-AI certification done within the target period, it is important to get the best CT-AI exam questions. You must try PDF4Test CT-AI practice exam that will help you get ISTQB CT-AI certification. PDF4Test hires the top industry experts to draft the Certified Tester AI Testing Exam (CT-AI) exam dumps and help the candidates to clear their CT-AI exam easily. PDF4Test plays a vital role in their journey to get the CT-AI certification.

ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4
  • 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 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 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 7
  • 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 8
  • systems from those required for conventional systems.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q55-Q60):

NEW QUESTION # 55
Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?
SELECT ONE OPTION

  • A. Test for human handover to give rest to the system.
  • B. Test for human handover after a given time interval.
  • C. Test for human handover when it should actually not be relinquishing control.
  • D. Test for human handover requiring mandatory relinquishing control.

Answer: C

Explanation:
* Testing Autonomy: Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems. The other tests are relevant to ensuring smooth operation and transitions between human and AI control.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Testing Autonomous AI-Based Systems and Testing for Human-AI Interaction.


NEW QUESTION # 56
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, II, and III
  • B. Aspects III, IV, and V
  • C. Aspects I, IV, and V
  • D. Aspects II, III and IV

Answer: B

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.
References:This explanation is aligned with the critical quality characteristics for AI-based systems as mentioned in the ISTQB CT-AI syllabus, focusing on the certification of medical devices.


NEW QUESTION # 57
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 evolve to optimize its routing
  • C. The robot must record the time of each delivery which is compiled into a report
  • D. The robot must recharge for no more than six hours a day

Answer: B

Explanation:
AI-based systems have specific quality characteristics, includingevolution,autonomy, andadaptability. A test objective that evaluates whether an AI systemevolvesto improve performance over time directly aligns with AI quality characteristics.
Explanation of Answer Choices:
* Option A: The robot must evolve to optimize its routing.
* Correct.Evolution is an AI quality characteristic that ensures the systemlearns from past experiencesand adapts to improve efficiency.
* Option B: The robot must recharge for no more than six hours a day.
* Incorrect.This is an operational constraint rather than an AI-specific quality characteristic.
* Option C: The robot must record the time of each delivery which is compiled into a report.
* Incorrect.Logging data does not relate to AI quality characteristics likeadaptability or autonomy.
* Option D: The robot must complete 99.99% of its deliveries each day.
* Incorrect.This is a performance target rather than an AI quality characteristic.
ISTQB CT-AI Syllabus References:
* Evolution as an AI Quality Characteristic:"Check how well the system learns from its own experience. Check how well the system copes when the profile of data changes (i.e., concept drift)".
Thus,Option A is the best choice as it directly tests an AI quality characteristic (evolution) in the upgraded autonomous robot.


NEW QUESTION # 58
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION

  • A. Data testing
  • B. Deploying the model
  • C. Tuning the model
  • D. Evaluating the model

Answer: C

Explanation:
Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.
Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.
Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.
Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.
Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.
Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters is C. Tuning the model.
Reference:
ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set.
Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.


NEW QUESTION # 59
Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
SELECT ONE OPTION

  • A. Sign change coverage
  • B. Neuron coverage
  • C. Value coverage
  • D. Threshold coverage

Answer: A

Explanation:
* Coverage for Neuron Activation Values: Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 6.2 Coverage Measures for Neural Networks, which details different types of coverage measures, including sign change coverage.


NEW QUESTION # 60
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