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ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| ML Functional Performance Metrics | 11% | - ROC, AUC, MSE, silhouette coefficient - Confusion matrix, accuracy, precision, recall |
| Introduction to AI | 10% | - AI technologies and frameworks
|
| Machine Learning (ML) Overview | 11% | - Supervised, unsupervised, reinforcement learning - ML workflow, overfitting, underfitting |
| Testing AI-Based Systems | 11% | - Test strategy and approach - Specific challenges and risks |
| Test Environment for AI Systems | 2% | - Data and infrastructure requirements |
| Testing Quality Characteristics | 11% | - Explainability and reliability testing - Testing transparency, fairness, robustness |
| Neural Networks and Testing | 4% | - Structure of neural networks - Coverage measures for deep learning |
| Quality Characteristics for AI-Based Systems | 10% | - Flexibility, adaptability, autonomy - Ethics, bias, transparency and safety |
| AI-Based System Testing Methods | 17% | - Adversarial testing, bias testing - Model validation and verification |
| Using AI for Testing Activities | 10% | - Test case generation, defect prediction - Regression optimization, test analysis |
| ML Data | 10% | - Data acquisition, preprocessing, labeling - Data quality issues and impact |
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
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 attacks can't be prevented by retraining the model with these examples augmented tothe training data.
- B. These examples are model specific and are not likely to cause another model trained on same task to fail.
- C. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
- D. Black box attacks based on adversarial examples create an exact duplicate model of the original.
Correct Answer: B 🗳️
Explanation: Only visible for Test4Engine members. You can sign-up / login (it's free).
"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.
Correct Answer: A 🗳️
Explanation: Only visible for Test4Engine members. You can sign-up / login (it's free).
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION
- A. Data for the use case is being generated at a fast pace.
- B. Compliance needs require proper care to be taken of input personal data.
- C. Test data being sourced from public sources.
- D. Nature of data constantly changes with lime.
Correct Answer: A 🗳️
Explanation: Only visible for Test4Engine members. You can sign-up / login (it's free).
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.
Correct Answer: B 🗳️
Explanation: Only visible for Test4Engine members. You can sign-up / login (it's free).
