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ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Machine Learning Fundamentals for Testers | - Supervised and unsupervised learning concepts - Model training and evaluation basics |
| Topic 2: Testing AI-Based Systems | - Bias, fairness, and explainability testing - Testing ML models - Data quality and dataset validation |
| Topic 3: Introduction to AI in Software Testing | - AI systems overview - Role of AI in testing |
| Topic 4: AI in Test Automation | - AI-based defect prediction and analytics - AI-supported test generation |
| Topic 5: AI System Quality Risks | - Model drift and performance degradation - Ethical and legal considerations |
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
Question 1
Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?
SELECT ONE OPTION
A. Test for human handover when it should actually not be relinquishing control.
B. Test for human handover after a given time interval.
C. Test for human handover requiring mandatory relinquishing control.
D. Test for human handover to give rest to the system.
Question 2
Which ONE of the following approaches to labelling requires the least time and effort?
SELECT ONE OPTION
A. Internal
B. Al-Assisted
C. Pre-labeled dataset
D. Outsourced
Question 3
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION
A. Testing the speed of the training of the model.
B. Testing the speed of the prediction by the model.
C. Testing the accuracy of the classification model.
D. Testing the API of the service powered by the ML model.
Question 4
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.
Solutions:
| Question 1 Answer: A | Question 2 Answer: C | Question 3 Answer: A | Question 4 Answer: B |
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