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ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Using AI for Testing Activities | 10% | - Test case generation, defect prediction - Regression optimization, test analysis |
| ML Functional Performance Metrics | 11% | - Confusion matrix, accuracy, precision, recall - ROC, AUC, MSE, silhouette coefficient |
| Quality Characteristics for AI-Based Systems | 10% | - Flexibility, adaptability, autonomy - Ethics, bias, transparency and safety |
| Introduction to AI | 10% | - AI definitions and types
|
| Machine Learning (ML) Overview | 11% | - Supervised, unsupervised, reinforcement learning - ML workflow, overfitting, underfitting |
| Testing Quality Characteristics | 11% | - Testing transparency, fairness, robustness - Explainability and reliability testing |
| Test Environment for AI Systems | 2% | - Data and infrastructure requirements |
| Testing AI-Based Systems | 11% | - Test strategy and approach - Specific challenges and risks |
| ML Data | 10% | - Data acquisition, preprocessing, labeling - Data quality issues and impact |
| AI-Based System Testing Methods | 17% | - Adversarial testing, bias testing - Model validation and verification |
| Neural Networks and Testing | 4% | - Structure of neural networks - Coverage measures for deep learning |
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) These examples are model specific and are not likely to cause another model trained on same task to fail.
D) Black box attacks based on adversarial examples create an exact duplicate model of the original.
2. "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) A lack of focus on choosing the right functional-performance metrics.
C) A lack of focus on non-functional requirements testing.
D) The input data has not been tested for quality prior to use for testing.
3. Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
SELECT ONE OPTION
A) Sign change coverage
B) Threshold coverage
C) Neuron coverage
D) Value coverage
4. In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.
SELECT ONE OPTION
A) Testing for bias
B) Testing for accuracy
C) Testing for security
D) Testing for concept drift
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: D |
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