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ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning (ML) Overview | 11% | - ML workflow, overfitting, underfitting - Supervised, unsupervised, reinforcement learning |
| ML Functional Performance Metrics | 11% | - ROC, AUC, MSE, silhouette coefficient - Confusion matrix, accuracy, precision, recall |
| Test Environment for AI Systems | 2% | - Data and infrastructure requirements |
| AI-Based System Testing Methods | 17% | - Model validation and verification - Adversarial testing, bias testing |
| Quality Characteristics for AI-Based Systems | 10% | - Flexibility, adaptability, autonomy - Ethics, bias, transparency and safety |
| ML Data | 10% | - Data acquisition, preprocessing, labeling - Data quality issues and impact |
| Testing Quality Characteristics | 11% | - Explainability and reliability testing - Testing transparency, fairness, robustness |
| Testing AI-Based Systems | 11% | - Specific challenges and risks - Test strategy and approach |
| Neural Networks and Testing | 4% | - Structure of neural networks - Coverage measures for deep learning |
| Using AI for Testing Activities | 10% | - Regression optimization, test analysis - Test case generation, defect prediction |
| Introduction to AI | 10% | - AI definitions and types
|
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
1. 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) Search of similar code based on natural language processing.
B) Clustering of similar code modules to predict based on similarity.
C) Identifying the relationship between developers and the modules developed by them.
D) Using a classification model to predict the presence of a defect by using code quality metrics as the input data.
2. Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION
A) A comparison of two different websites for the same company to observe from a user acceptance perspective.
B) A comparison of the performance of an ML system on two different input datasets.
C) A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
D) A comparison of the performance of two different ML implementations on the same input data.
3. Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
SELECT ONE OPTION
A) Training data - validation data - test data
B) Training data - validation data
C) Training data * test data
D) Validation data - test data
4. Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION
A) Testing the data pipeline for any sources for algorithmic bias.
B) Testing the distribution shift in the training data for inappropriate bias.
C) Check the input test data for potential sample bias.
D) Test the model during model evaluation for data bias.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: D |
Harvey 
Larry 
Alexia 
Helen 
Elva 
Lewis 
Abigail 
Alger 
Kristin 
Derrick 
