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ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| ML Functional Performance Metrics | - Performance Evaluation
|
| Quality Characteristics for AI-Based Systems | - AI-Specific Quality Attributes
|
| Test Environments for AI-Based Systems | - AI Test Infrastructure
|
| Methods and Techniques for Testing AI-Based Systems | - Test Design Techniques
|
| Testing AI-Specific Quality Characteristics | - Quality Validation
|
| Using AI for Testing | - AI-Assisted Testing
|
| ML Data | - Data Quality
|
| Testing AI-Based Systems Overview | - AI Testing Fundamentals
|
| ML Neural Networks and Testing | - Neural Networks
|
| Machine Learning | - ML Fundamentals
|
| Introduction to AI | - AI Fundamentals
|
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION
- A. Data for the use case is being generated at a fast pace.
- B. Nature of data constantly changes with lime.
- C. Test data being sourced from public sources.
- D. Compliance needs require proper care to be taken of input personal data.
Explanation: Only visible for PassReview members. You can sign-up / login (it's free).
Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?
SELECT ONE OPTION
- A. Feature selection
- B. Data labeling
- C. Data augmentation
- D. Data sampling
Explanation: Only visible for PassReview members. You can sign-up / login (it's free).
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. Machine learning on logs of execution
- B. GUI analysis by computer vision
- C. Natural language processing on textual requirements
- D. Analyzing source code for generating test cases
Explanation: Only visible for PassReview members. You can sign-up / login (it's free).
Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?
SELECT ONE OPTION
- A. Clustering of similar code modules to predict based on similarity.
- B. Using a classification model to predict the presence of a defect by using code quality metrics as the input data.
- C. Identifying the relationship between developers and the modules developed by them.
- D. Search of similar code based on natural language processing.
Explanation: Only visible for PassReview members. You can sign-up / login (it's free).
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