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ISQI CT-GenAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Fundamentals of Generative AI	20%	- AI Terminology 1. Transformer architecture 2. Tokens and prompts 3. Deep Learning 4. Large Language Models (LLMs) 5. Machine Learning (ML) 6. Generative AI (GenAI) 7. Artificial Intelligence (AI) - AI Development Lifecycle 1. Evaluation 2. Deployment and monitoring 3. Data collection, preparation, and curation 4. Model training and fine-tuning - Generative AI Concepts 1. Model types (Base, Instruction-tuned, RAG) 2. AI model behavior 3. Alignment and guardrails 4. Emergent capabilities and limitations 5. Hallucinations 6. Training data and context windows
Topic 2: Testing Activities for Generative AI	30%	- Traceability and Documentation 1. Test coverage of AI model components 2. Documentation requirements for AI testing - Model and Output Evaluation 1. Checkpoint testing 2. Human evaluation methods 3. Output correctness and quality assessment 4. Automated evaluation methods 5. Metamorphic testing - Prompt-Based Testing 1. Test case design using prompts 2. Prompt engineering basics 3. Test data creation with GenAI - Requirements-Based Testing 1. Non-functional requirements for AI-based systems 2. AI-related quality requirements 3. Functional requirements for AI-based systems
Topic 3: Risks and Testing Challenges for Generative AI	30%	- Quality Risks Specific to Generative AI 1. Dependency on external components 2. Offensive, harmful, or biased content 3. Inconsistent responses across runs 4. Incorrect or fabricated outputs (hallucinations) 5. Inappropriate output for the context 6. Input sensitivity (prompt brittleness) - Testing Challenges for Generative AI 1. Subjectivity of quality assessment 2. Non-deterministic output behavior 3. Test oracle problem 4. Complexity of the AI component 5. Regulatory and compliance considerations 6. Coverage challenges 7. Ethical testing concerns
Topic 4: Tools for Testing Generative AI	20%	- Using Tools for Common Testing Activities 1. Security testing tools 2. Simulation and monitoring tools 3. Model evaluation tools 4. Prompt testing tools - Testing Tools Overview 1. Categories of GenAI testing tools 2. Selecting appropriate tools for specific testing needs

ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:

1. In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.

A) ii, iii, iv
B) i, ii, iii
C) i, iii, v
D) i, ii, iii (Duplicate entry in original source)

2. You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

A) Add a step to review the change log for syntax errors before analysis.
B) Include references to version control systems like Git in the constraints.
C) Specify that the role is a test architect specializing in CI/CD pipelines.
D) Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity

3. What is a hallucination in LLM outputs?

A) A systematic preference learned from data
B) Generation of factually incorrect content for the task
C) A logical mistake in multi-step deduction
D) A transient network failure during inference

4. The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

A) Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
B) Precision for anomaly identification and recall for coverage of valid/invalid partitions
C) Diversity for anomaly identification and precision for partitions
D) Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions

5. You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?

A) Emphasize constraints that focus on deviations that could impact release timelines or quality gates.
B) Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.
C) Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.
D) Specify that the role is a test analyst with expertise in predictive analytics and risk management.

Solutions:

Question # 1
Answer: B

Question # 2
Answer: D

Question # 3
Answer: B

Question # 4
Answer: B

Question # 5
Answer: B