How to Test AI-Based Systems Micro-Credential

AT*SQA Software Testing Micro-Credential

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How to Test AI-Based Systems Micro-Credential

This micro-credential shows you can plan and explain practical tests for AI-based systems. That includes data testing, model testing, component testing, integration testing, system testing, and acceptance testing, plus AI-focused techniques such as adversarial attack testing, data poisoning testing, pairwise testing, back-to-back testing, A/B testing, metamorphic testing, exploratory testing, and checklist-based testing. Passing adds you to the Official U.S. List of Certified Testers and counts toward the AT*SQA AI for Testers certification.

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AT*SQA How to Test AI-Based Systems Body of Knowledge (Syllabus)

Register for the How to Test AI-Based Systems Micro-Credential Exam

What the How to Test AI-Based Systems Micro-Credential Covers

This credential is about testing systems that include AI. It focuses on the practical problems testers face when requirements are unclear, outputs are probabilistic, acceptance criteria are flexible, and the system may continue learning after release.

The syllabus explains how traditional test levels apply to AI-based systems, including data testing, model testing, component testing, integration testing, system testing, and acceptance testing. It also shows why the AI parts of the system need special attention to training data, validation data, test data, model behavior, and the data pipeline into and out of the model.

You will learn how to evaluate data quality issues such as wrong, incomplete, mislabeled, insufficient, obsolete, unbalanced, unfair, duplicate, irrelevant, private, or insecure data. You will also learn how model testing uses measures such as accuracy, precision, recall, and F1-score, along with qualities such as resource use, adaptability, and transparency.

The credential covers AI-focused testing techniques, including adversarial attack testing, data poisoning testing, pairwise testing, back-to-back testing, A/B testing, metamorphic testing, exploratory testing, and checklist-based testing. It also covers test oracles, acceptance criteria, test environments, autonomous systems, non-deterministic systems, complex AI-based systems, and testing for transparency, interpretability, and explainability.

What You Will Learn in the How to Test AI-Based Systems Micro-Credential

• Why AI-based systems are difficult to test when requirements and acceptance criteria are not exact
• How to test the data used for training, validation, and model testing
• How data quality issues can lead to reduced accuracy, bias, or a compromised model
• How to apply component testing, integration testing, system testing, and acceptance testing to AI-based systems
• How to test model performance using criteria such as accuracy, precision, recall, and F1-score
• How to test data pipelines into and out of an AI model
• How to use adversarial attack testing and data poisoning testing
• How pairwise testing helps reduce large sets of AI input parameters
• How back-to-back testing, A/B testing, and metamorphic testing help when exact expected results are hard to define
• How exploratory testing, error guessing, and checklist-based testing apply to AI systems
• How to test autonomous, non-deterministic, and complex AI-based systems
• How to test for transparency, interpretability, and explainability
• How test oracles work when AI outputs may have a range of acceptable answers
• How to define acceptance criteria for adaptability, flexibility, evolution, autonomy, bias, ethics, and explainability
• How to plan test environments for AI systems that depend on data, hardware, simulations, and continuous monitoring

Who Should Earn the How to Test AI-Based Systems Micro-Credential

This credential is for software testers, QA analysts, test managers, automation engineers, and anyone who needs to test software that includes AI or machine learning. It is especially useful for testers who need a practical way to discuss data quality, model behavior, test oracles, acceptance criteria, and post-release testing with developers, data scientists, product owners, and business stakeholders.

No formal AI testing experience is required. Familiarity with basic testing concepts is helpful, and the AI Introduction for Testers and What to Test in AI-Based Systems micro-credentials provide useful background.

How to Earn This Credential

Download the free syllabus to study, then register for the exam on the purchase page for $39. If you want structured video instruction and exam prep materials, the AT*Learn How to Test AI-Based Systems training is available for $49.

Passing the exam adds you to the Official U.S. List of Certified and Credentialed Software Testers, which employers use to verify credentials. This credential is the third of four micro-credentials in the AT*SQA AI for Testers certification. The others are AI Introduction for Testers, What to Test in AI-Based Systems, and Testing Using AI. AT*SQA also offers the ISTQB AI Testing and ISTQB Testing with Generative AI certifications for testers who want a broader AI testing certification.

How to Test AI-Based Systems: Common Questions

How is this different from What to Test in AI-Based Systems?
What to Test in AI-Based Systems focuses on identifying the risks and quality characteristics that matter in AI-based systems. How to Test AI-Based Systems focuses on the testing approach: test levels, data testing, model testing, AI-focused test techniques, test oracles, acceptance criteria, and test environments.

How is this different from Testing Using AI?
How to Test AI-Based Systems focuses on testing a product or system that contains AI. Testing Using AI focuses on using AI tools to help perform testing work, such as generating test cases, creating test data, maintaining automation, and analyzing defects.

Do traditional testing techniques still matter when testing AI-based systems?
Yes. AI-based systems usually contain non-AI components that still require traditional testing. Traditional test levels and experience-based testing still apply, but testers also need AI-focused techniques such as data testing, model testing, adversarial testing, data poisoning testing, A/B testing, back-to-back testing, and metamorphic testing.

Does this credential cover AI security and bias risks?
Yes. The syllabus covers adversarial attack testing, data poisoning testing, freedom from inappropriate bias, ethics, privacy issues, security issues, and the way poor data quality can reduce accuracy, create bias, or compromise the model.

Do I need to know how to code to earn this credential?
No. The syllabus is focused on understanding how to test AI-based systems. Some topics are technical, including data pipelines, models, automation, and test environments, but the credential does not require writing code.