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ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Quality Characteristics for AI-Based Systems | - AI-Specific Quality Attributes
|
| ML Functional Performance Metrics | - Performance Evaluation
|
| Testing AI-Specific Quality Characteristics | - Quality Validation
|
| ML Neural Networks and Testing | - Neural Networks
|
| Methods and Techniques for Testing AI-Based Systems | - Test Design Techniques
|
| Testing AI-Based Systems Overview | - AI Testing Fundamentals
|
| Test Environments for AI-Based Systems | - AI Test Infrastructure
|
| ML Data | - Data Quality
|
| Using AI for Testing | - AI-Assisted Testing
|
| Machine Learning | - ML Fundamentals
|
| Introduction to AI | - AI Fundamentals
|
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
1. 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) Analyzing source code for generating test cases
C) GUI analysis by computer vision
D) Natural language processing on textual requirements
2. Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION
A) Testing the accuracy of the classification model.
B) Testing the API of the service powered by the ML model.
C) Testing the speed of the prediction by the model.
D) Testing the speed of the training of the model.
3. Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION
A) Self-learning
B) Non-determinism
C) Robustness
D) High complexity
4. "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 similarity between the training and testing data.
B) The input data has not been tested for quality prior to use for testing.
C) A lack of focus on choosing the right functional-performance metrics.
D) A lack of focus on non-functional requirements testing.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: A |


