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ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Test Environment for AI Systems | 2% | - Data and infrastructure requirements |
| Testing AI-Based Systems | 11% | - Specific challenges and risks - Test strategy and approach |
| ML Data | 10% | - Data acquisition, preprocessing, labeling - Data quality issues and impact |
| ML Functional Performance Metrics | 11% | - Confusion matrix, accuracy, precision, recall - ROC, AUC, MSE, silhouette coefficient |
| Introduction to AI | 10% | - AI definitions and types
|
| Using AI for Testing Activities | 10% | - Regression optimization, test analysis - Test case generation, defect prediction |
| Machine Learning (ML) Overview | 11% | - Supervised, unsupervised, reinforcement learning - ML workflow, overfitting, underfitting |
| Neural Networks and Testing | 4% | - Structure of neural networks - Coverage measures for deep learning |
| Quality Characteristics for AI-Based Systems | 10% | - Ethics, bias, transparency and safety - Flexibility, adaptability, autonomy |
| AI-Based System Testing Methods | 17% | - Adversarial testing, bias testing - Model validation and verification |
| Testing Quality Characteristics | 11% | - Explainability and reliability testing - Testing transparency, fairness, robustness |
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
1. Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION
A) These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
B) These attacks can't be prevented by retraining the model with these examples augmented tothe training data.
C) Black box attacks based on adversarial examples create an exact duplicate model of the original.
D) These examples are model specific and are not likely to cause another model trained on same task to fail.
2. "Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.
Which ONE of the following combinations requires MAXIMIZATION?
SELECT ONE OPTION
A) Maximize recall and precision
B) Maximize precision and accuracy
C) Maximize accuracy and recall
D) Maximize specificity number of classes
3. Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION
A) The number of classes
B) Biased data
C) The data pipeline
D) The quality of the labeling
4. 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) Data sampling
B) Data labeling
C) Feature selection
D) Data augmentation
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: A |
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