ISQI CT-AI_v1.0_World Questions & Answers - in .pdf
- Total Q&A: 40
- Update: Sep 09, 2026
- Price: $59.99
- Vendor: ISQI
- 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 Q&A - Testing Engine
- Total Q&A: 40
- Update: Sep 09, 2026
- Price: $59.99
- Vendor: ISQI
- 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 Overview:
| Certification Vendor: | iSQI / ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester AI Testing (v1.0) |
| Exam Number: | CT-AI_v1.0_World |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| Available Languages: | French, Spanish, German, English, Polish, Brazilian Portuguese |
| Exam Duration: | 60 (75 for non-native language) |
| Exam Price: | US$87.29 - US$213.00 / €240.00 (varies by region) |
| Exam Format: | Multiple Choice Questions (single/multiple answer) |
| Certificate Validity Period: | Lifetime |
| Passing Score: | 65% (31 out of 47 points) |
| Real Exam Qty: | 40 |
| Recommended Training: | iSQI Training Partners ISTQB Accredited Training Providers |
| Exam Registration: | iSQI Official Registration Pearson VUE |
| 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: Using AI for Testing Activities | 10% | - Regression optimization, test analysis - Test case generation, defect prediction |
| Topic 2: ML Functional Performance Metrics | 11% | - ROC, AUC, MSE, silhouette coefficient - Confusion matrix, accuracy, precision, recall |
| Topic 3: ML Data | 10% | - Data quality issues and impact - Data acquisition, preprocessing, labeling |
| Topic 4: Introduction to AI | 10% | - AI technologies and frameworks
|
| Topic 5: Testing Quality Characteristics | 11% | - Testing transparency, fairness, robustness - Explainability and reliability testing |
| Topic 6: Test Environment for AI Systems | 2% | - Data and infrastructure requirements |
| Topic 7: Testing AI-Based Systems | 11% | - Specific challenges and risks - Test strategy and approach |
| Topic 8: Quality Characteristics for AI-Based Systems | 10% | - Ethics, bias, transparency and safety - Flexibility, adaptability, autonomy |
| Topic 9: Neural Networks and Testing | 4% | - Structure of neural networks - Coverage measures for deep learning |
| Topic 10: Machine Learning (ML) Overview | 11% | - ML workflow, overfitting, underfitting - Supervised, unsupervised, reinforcement learning |
| Topic 11: AI-Based System Testing Methods | 17% | - Model validation and verification - Adversarial testing, bias testing |
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
Question #1
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 the current neural network ML model on the current data set with a slightly modified data set.
C. 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.
D. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
Question #2
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION
A. Test data being sourced from public sources.
B. Nature of data constantly changes with lime.
C. Compliance needs require proper care to be taken of input personal data.
D. Data for the use case is being generated at a fast pace.
Question #3
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION
A. I and III
B. Only II
C. I and II
D. Only III
Question #4
Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large?
SELECT ONE OPTION
A. Using of a random subset of tests.
B. Using an Al-based tool to optimize the regression test suite by analyzing past test results
C. Identifying suitable tests by looking at the complexity of the test cases.
D. Automating test scripts using Al-based test automation tools.
Solutions:
| Question #1 Answer: D | Question #2 Answer: D | Question #3 Answer: C | Question #4 Answer: B |
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