Last Updated: Oct 05, 2026
No. of Questions: 40 Questions & Answers with Testing Engine
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| Section | Objectives |
|---|---|
| AI System Quality Risks | - Model drift and performance degradation - Ethical and legal considerations |
| AI in Test Automation | - AI-based defect prediction and analytics - AI-supported test generation |
| Introduction to AI in Software Testing | - Role of AI in testing - AI systems overview |
| Machine Learning Fundamentals for Testers | - Supervised and unsupervised learning concepts - Model training and evaluation basics |
| Testing AI-Based Systems | - Data quality and dataset validation - Testing ML models - Bias, fairness, and explainability testing |
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
Correct Answer: B 🗳️
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Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION
Correct Answer: C 🗳️
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Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION
Correct Answer: B 🗳️
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Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION
Correct Answer: D 🗳️
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