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ISQI ISTQB Certified Tester AI Testing (v1.0) : CT-AI_v1.0_World

Updated: Oct 13, 2026

Q & A: 40 Questions and Answers

CT-AI_v1.0_World Braindumps VCE
  • Exam Code: CT-AI_v1.0_World
  • Exam Name: ISTQB Certified Tester AI Testing (v1.0)

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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

SectionObjectives
Topic 1: AI in Test Automation- AI-based defect prediction and analytics
- AI-supported test generation
Topic 2: AI System Quality Risks- Ethical and legal considerations
- Model drift and performance degradation
Topic 3: Introduction to AI in Software Testing- AI systems overview
- Role of AI in testing
Topic 4: Machine Learning Fundamentals for Testers- Model training and evaluation basics
- Supervised and unsupervised learning concepts
Topic 5: Testing AI-Based Systems- Bias, fairness, and explainability testing
- Testing ML models
- Data quality and dataset validation

ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:

Question #1

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. Accuracy issues
  • B. Privacy issues
  • C. Bias issues
  • D. Security issues
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #2

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

  • A. Pre-labeled dataset
  • B. Internal
  • C. Al-Assisted
  • D. Outsourced
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #3

Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION

  • A. Clustering is supervised learning.
  • B. Clustering is classification of a continuous quantity.
  • C. Clustering requires you to know the classes.
  • D. Clustering is done without prior knowledge of output classes.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

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 data pipeline for any sources for algorithmic bias.
  • B. Check the input test data for potential sample bias.
  • C. Test the model during model evaluation for data bias.
  • D. Testing the distribution shift in the training data for inappropriate bias.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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