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Instant Download ISQI : CT-AI_v1.0_World Questions & Answers as PDF & Test Engine
- Exam Code: CT-AI_v1.0_World
- Exam Name: ISTQB Certified Tester AI Testing (v1.0)
- Updated: Sep 05, 2026
- No. of Questions: 40 Questions and Answers
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ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Testing AI-Based Systems Overview | - AI Testing Fundamentals
|
| Topic 2: Test Environments for AI-Based Systems | - AI Test Infrastructure
|
| Topic 3: Using AI for Testing | - AI-Assisted Testing
|
| Topic 4: ML Data | - Data Quality
|
| Topic 5: Methods and Techniques for Testing AI-Based Systems | - Test Design Techniques
|
| Topic 6: Introduction to AI | - AI Fundamentals
|
| Topic 7: Machine Learning | - ML Fundamentals
|
| Topic 8: ML Neural Networks and Testing | - Neural Networks
|
| Topic 9: Quality Characteristics for AI-Based Systems | - AI-Specific Quality Attributes
|
| Topic 10: ML Functional Performance Metrics | - Performance Evaluation
|
| Topic 11: Testing AI-Specific Quality Characteristics | - Quality Validation
|
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION
- A. The quality of the labeling
- B. The data pipeline
- C. The number of classes
- D. Biased data
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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. GUI analysis by computer vision
- B. Natural language processing on textual requirements
- C. Machine learning on logs of execution
- D. Analyzing source code for generating test cases
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION
- A. Black box attacks based on adversarial examples create an exact duplicate model of the original.
- B. These examples are model specific and are not likely to cause another model trained on same task to fail.
- C. These attacks can't be prevented by retraining the model with these examples augmented tothe training data.
- D. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
SELECT ONE OPTION
- A. Validation data - test data
- B. Training data * test data
- C. Training data - validation data
- D. Training data - validation data - test data
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
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