1500 Questions | ISTQB AI Testing Certification (CT-AI) 2026

Testing AI-Powered Applications (40%): Deep-diving into the validation of conversational AI, Natural Language Processing (NLP) accuracy, and functional testing of ML models.

AI Testing Methodologies and Tools (20%): Adapting the software development life cycle (SDLC) for AI and integrating specialized testing frameworks into modern DevOps pipelines.

Specialized Topics and Industry Trends (20%): Navigating the complexities of AI in Cloud/IoT environments and mastering the critical task of identifying bias and ensuring diversity in AI outputs.

B. It occurs when the statistical properties of the target variable change over time, making predictions less accurate,

C. It is a term for when the developer changes the programming language of the application,

D. It describes the user interface getting outdated and requiring a refresh,

E. It is a security flaw where an attacker injects malicious code into the training set,

F. It refers to the model becoming faster at processing data than initially tested,

Correct Answer: B

Explanation:

B (Correct): Concept Drift is a major AI testing topic; it signifies that the model's environment has changed, requiring retraining or re-validation,

C (Incorrect): Language changes are refactoring issues, not "drift",

D (Incorrect): UI changes are part of traditional maintenance, not AI-specific drift,

E (Incorrect): This describes "Data Poisoning," a different security-focused AI topic,

F (Incorrect): Increased speed is a performance metric, not a drift in conceptual accuracy,

Question 2: Which testing technique is most appropriate for verifying that a Conversational AI (Chatbot) handles diverse linguistic inputs without showing demographic bias?

B. Metamorphic Testing,

C. Unit Testing the API endpoint,

D. Regression Testing of the database,

E. Load Testing the server capacity,

F. Black-box testing of the power supply,

Correct Answer: B

Explanation:

A (Incorrect): Stress testing looks for breaking points under high volume, not bias,

C (Incorrect): Unit testing verifies code logic but rarely catches complex linguistic bias in a model,

D (Incorrect): Database regression doesn't address the probabilistic nature of NLP outputs,

E (Incorrect): Load testing measures performance, not fairness or diversity,

F (Incorrect): Power supply testing is irrelevant to software-level AI bias,

Question 3: When testing an AI-powered application in a DevOps environment, which stage is most critical for "Data Validation" to prevent skewed model performance?

B. The automated deployment to production,

C. The Data Pre-processing and Ingestion phase,

D. The creation of the marketing documentation,

E. The CSS styling phase of the front end,

F. The final archival of the project source code,

Correct Answer: C

Explanation:

A (Incorrect): Meetings are for communication, not technical data validation,

B (Incorrect): While deployment is important, validating data at this stage is often too late to prevent core model errors,

D (Incorrect): Documentation does not affect the technical performance of the ML model,

E (Incorrect): UI styling is independent of the underlying machine learning logic,

F (Incorrect): Archival happens after the project is complete, far past the validation window,

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