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


"ISTQB Certified Tester AI Testing (v1.0)", also known as CT-AI_v1.0_World exam, is a ISQI Certification. With the complete collection of questions and answers, PrepAwayTest has assembled to take you through 40 Q&As to your CT-AI_v1.0_World Exam preparation. In the CT-AI_v1.0_World exam resources, you will cover every field and category in AI Testing Certification helping to ready you for your successful ISQI Certification.

  • Exam Code: CT-AI_v1.0_World
  • Exam Name: ISTQB Certified Tester AI Testing (v1.0)
  • Total Questions: 40
  • Certification Provider: ISQI
  • Corresponding Certification: AI Testing
  • Updated on: Sep 11, 2026

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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

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

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

Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION

  • A. Machine learning on logs of execution
  • B. GUI analysis by computer vision
  • C. Analyzing source code for generating test cases
  • D. Natural language processing on textual requirements
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).

Which ONE of the following describes a situation of back-to-back testing the LEAST?
SELECT ONE OPTION

  • A. Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data
  • B. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
  • C. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
  • D. Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).

Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION

  • A. Nature of data constantly changes with lime.
  • B. Data for the use case is being generated at a fast pace.
  • C. Compliance needs require proper care to be taken of input personal data.
  • D. Test data being sourced from public sources.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).

Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?
SELECT ONE OPTION

  • A. Using a vision-based detection of the GUI layout changes before and after test object changes.
  • B. Using a pixel comparison of the GUI before and after the change to check the differences.
  • C. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
  • D. Using a computer vision to compare the GUI before and after the test object changes.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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