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ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: ML Data | 10% | - Data acquisition, preprocessing, labeling - Data quality issues and impact |
| Topic 2: Using AI for Testing Activities | 10% | - Regression optimization, test analysis - Test case generation, defect prediction |
| Topic 3: Testing AI-Based Systems | 11% | - Specific challenges and risks - Test strategy and approach |
| Topic 4: ML Functional Performance Metrics | 11% | - ROC, AUC, MSE, silhouette coefficient - Confusion matrix, accuracy, precision, recall |
| Topic 5: Introduction to AI | 10% | - AI definitions and types
|
| Topic 6: Test Environment for AI Systems | 2% | - Data and infrastructure requirements |
| Topic 7: Machine Learning (ML) Overview | 11% | - Supervised, unsupervised, reinforcement learning - ML workflow, overfitting, underfitting |
| Topic 8: Quality Characteristics for AI-Based Systems | 10% | - Flexibility, adaptability, autonomy - Ethics, bias, transparency and safety |
| Topic 9: AI-Based System Testing Methods | 17% | - Adversarial testing, bias testing - Model validation and verification |
| Topic 10: Neural Networks and Testing | 4% | - Coverage measures for deep learning - Structure of neural networks |
| Topic 11: Testing Quality Characteristics | 11% | - Explainability and reliability testing - Testing transparency, fairness, robustness |
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
Question #1
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION
A. Evaluating the model
B. Tuning the model
C. Deploying the model
D. Data testing
Question #2
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
B. Validation data - test data
C. Training data * test data
D. Training data - validation data - test data
Question #3
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION
A. Only II
B. Only III
C. I and II
D. I and III
Question #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. Test the model during model evaluation for data bias.
C. Testing the distribution shift in the training data for inappropriate bias.
D. Check the input test data for potential sample bias.
Solutions:
| Question #1 Correct Answer: B | Question #2 Correct Answer: D | Question #3 Correct Answer: C | Question #4 Correct Answer: B |
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