Study with CT-AI_v1.0_World Actual Test Questions
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Updated: Sep 10, 2026
No. of Questions: 40 Questions & Answers with Testing Engine
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ISQI CT-AI_v1.0_World Exam Overview:
| Certification Vendor: | iSQI / ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester AI Testing (v1.0) |
| Exam Number: | CT-AI_v1.0_World |
| Available Languages: | French, German, English, Polish, Spanish, Brazilian Portuguese |
| Real Exam Qty: | 40 |
| Certificate Validity Period: | Lifetime |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| Exam Duration: | 60 (75 for non-native language) |
| Exam Format: | Multiple Choice Questions (single/multiple answer) |
| Passing Score: | 65% (31 out of 47 points) |
| Exam Price: | US$87.29 - US$213.00 / €240.00 (varies by region) |
| Recommended Training: | ISTQB Accredited Training Providers iSQI Training Partners |
| Exam Registration: | Pearson VUE iSQI Official Registration |
| Sample Questions: | ISQI CT-AI_v1.0_World Sample Questions |
| Exam Way: | In-person test center or online remote proctored (FLEX exam) |
| Pre Condition: | ISTQB Certified Tester Foundation Level (CTFL) certification required |
| Official Syllabus URL: | https://istqb.org/certifications/certified-tester-ai-testing-ct-ai-retiring/ |
ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Neural Networks and Testing | 4% | - Coverage measures for deep learning - Structure of neural networks |
| Topic 2: Introduction to AI | 10% | - AI definitions and types
|
| Topic 3: Using AI for Testing Activities | 10% | - Test case generation, defect prediction - Regression optimization, test analysis |
| Topic 4: ML Functional Performance Metrics | 11% | - ROC, AUC, MSE, silhouette coefficient - Confusion matrix, accuracy, precision, recall |
| Topic 5: Quality Characteristics for AI-Based Systems | 10% | - Flexibility, adaptability, autonomy - Ethics, bias, transparency and safety |
| Topic 6: Testing Quality Characteristics | 11% | - Testing transparency, fairness, robustness - Explainability and reliability testing |
| Topic 7: AI-Based System Testing Methods | 17% | - Adversarial testing, bias testing - Model validation and verification |
| Topic 8: Test Environment for AI Systems | 2% | - Data and infrastructure requirements |
| Topic 9: ML Data | 10% | - Data acquisition, preprocessing, labeling - Data quality issues and impact |
| Topic 10: Testing AI-Based Systems | 11% | - Test strategy and approach - Specific challenges and risks |
| Topic 11: Machine Learning (ML) Overview | 11% | - Supervised, unsupervised, reinforcement learning - ML workflow, overfitting, underfitting |
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. Analyzing source code for generating test cases
- B. Natural language processing on textual requirements
- C. Machine learning on logs of execution
- D. GUI analysis by computer vision
Correct Answer: B 🗳️
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The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION
- A. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
- B. Individual bias at the neuron level, and weights assigned to the connections between theneurons.
- C. Individual bias at the neuron level, and activation values of neurons in the previous layer.
- D. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
Correct Answer: D 🗳️
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Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
SELECT ONE OPTION
- A. The challenge of providing explainability to the decisions made by the system.
- B. Challenges in the creation of scenarios of human handover for autonomous systems.
- C. Challenges resulting from low accuracy of the models.
- D. The challenge of mimicking undefined scenarios generated due to self-learning
Correct Answer: B 🗳️
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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. Neuron coverage
- B. Sign change coverage
- C. Value coverage
- D. Threshold coverage
Correct Answer: B 🗳️
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