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ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| AI System Quality Risks | - Ethical and legal considerations - Model drift and performance degradation |
| Testing AI-Based Systems | - Bias, fairness, and explainability testing - Data quality and dataset validation - Testing ML models |
| Introduction to AI in Software Testing | - Role of AI in testing - AI systems overview |
| AI in Test Automation | - AI-based defect prediction and analytics - AI-supported test generation |
| Machine Learning Fundamentals for Testers | - Model training and evaluation basics - Supervised and unsupervised learning concepts |
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
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.
Correct Answer: D 🗳️
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Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
SELECT ONE OPTION
- A. The challenge of providing explainability to the decisions made by the system.
- B. Challenges in the creation of scenarios of human handover for autonomous systems.
- C. Challenges resulting from low accuracy of the models.
- D. The challenge of mimicking undefined scenarios generated due to self-learning
Correct Answer: B 🗳️
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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. Black box attacks based on adversarial examples create an exact duplicate model of the original.
- C. These examples are model specific and are not likely to cause another model trained on same task to fail.
- D. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
Correct Answer: C 🗳️
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"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 focus on choosing the right functional-performance metrics.
- B. A lack of similarity between the training and testing data.
- C. The input data has not been tested for quality prior to use for testing.
- D. A lack of focus on non-functional requirements testing.
Correct Answer: B 🗳️
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