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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 |
| Exam Duration: | 60 (75 for non-native language) |
| Certificate Validity Period: | Lifetime |
| Exam Price: | US$87.29 - US$213.00 / €240.00 (varies by region) |
| Exam Format: | Multiple Choice Questions (single/multiple answer) |
| Passing Score: | 65% (31 out of 47 points) |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| Available Languages: | English, German, Brazilian Portuguese, French, Polish, Spanish |
| 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 |
|---|---|---|
| Test Environment for AI Systems | 2% | - Data and infrastructure requirements |
| Testing AI-Based Systems | 11% | - Test strategy and approach - Specific challenges and risks |
| ML Data | 10% | - Data acquisition, preprocessing, labeling - Data quality issues and impact |
| AI-Based System Testing Methods | 17% | - Adversarial testing, bias testing - Model validation and verification |
| ML Functional Performance Metrics | 11% | - ROC, AUC, MSE, silhouette coefficient - Confusion matrix, accuracy, precision, recall |
| Testing Quality Characteristics | 11% | - Testing transparency, fairness, robustness - Explainability and reliability testing |
| Introduction to AI | 10% | - AI technologies and frameworks
|
| Quality Characteristics for AI-Based Systems | 10% | - Flexibility, adaptability, autonomy - Ethics, bias, transparency and safety |
| Neural Networks and Testing | 4% | - Coverage measures for deep learning - Structure of neural networks |
| Using AI for Testing Activities | 10% | - Regression optimization, test analysis - Test case generation, defect prediction |
| Machine Learning (ML) Overview | 11% | - ML workflow, overfitting, underfitting - Supervised, unsupervised, reinforcement learning |
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION
- A. Genetic algorithms
- B. Case control structures
- C. Procedural programming
- D. Search engines
Correct Answer: A 🗳️
Explanation: Only visible for BraindumpQuiz members. You can sign-up / login (it's free).
A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION
- A. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
- B. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
- C. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
- D. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
Correct Answer: A 🗳️
Explanation: Only visible for BraindumpQuiz members. You can sign-up / login (it's free).
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION
- A. Robustness
- B. High complexity
- C. Self-learning
- D. Non-determinism
Correct Answer: A 🗳️
Explanation: Only visible for BraindumpQuiz members. You can sign-up / login (it's free).
"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION
- A. A lack of focus on non-functional requirements testing.
- B. A lack of similarity between the training and testing data.
- C. The input data has not been tested for quality prior to use for testing.
- D. A lack of focus on choosing the right functional-performance metrics.
Correct Answer: B 🗳️
Explanation: Only visible for BraindumpQuiz members. You can sign-up / login (it's free).
By Miriam
