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ISQI CT-GenAI Exam Syllabus Topics:

Section	Objectives
Topic 1: Application of GenAI in Software Testing	- Practical use in testing workflows 1. Test case generation using LLMs 2. Regression suite optimization 3. Defect report analysis and summarization 4. Test data generation and augmentation
Topic 2: Organizational Adoption and Governance	- Enterprise GenAI adoption 1. LLMOps and governance models 2. Policy, ethics, and compliance considerations 3. Integration into CI/CD pipelines
Topic 3: Risk, Quality, and Limitations of GenAI	- Risks in GenAI usage 1. Bias and fairness issues 2. Hallucinations and reasoning errors 3. Data privacy and security concerns 4. Environmental and energy considerations
Topic 4: Foundations of Generative AI and LLMs	- Introduction to Generative AI in Software Testing 1. Difference between chatbots and LLM-based test tools 2. LLM basics, tokenization, context window, multimodal models
Topic 5: Prompt Engineering for Testing	- Prompt design techniques 1. Structuring prompts for test case generation 2. Prompt chaining and meta prompting 3. Zero-shot, one-shot, few-shot prompting

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

1. You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?

A) Generate prioritized conditions in one shot -> verify coverage
B) Generate conditions -> prioritize by risk -> map to requirements to find gaps
C) Detect requirement defects -> generate conditions -> prioritize
D) Prioritize requirements -> generate conditions -> review defects

2. Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

A) The number of tokens processed directly determines the carbon intensity of each query
B) The location of the data center determines model bias and accuracy levels
C) The duration of user sessions primarily affects latency but not power efficiency
D) The type of cloud platform affects processing speed but not total energy draw

3. A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

A) Data exfiltration
B) Malicious code generation
C) Request manipulation
D) Data poisoning

4. Which AI approach requires feature engineering and structured data preparation?

A) Classical Machine Learning
B) Deep Learning
C) Generative AI
D) Symbolic AI

5. Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

A) Select models with maximum vendor visibility and strong online presence to ensure reliability
B) Select LLMs aligned to measurable test outcomes, compatible with current infrastructure
C) Select open-source models prioritizing creativity over compliance or performance consistency
D) Select broad-coverage models offering diverse functionalities for various test scenarios

Solutions:

Question # 1
Answer: B

Question # 2
Answer: A

Question # 3
Answer: C

Question # 4
Answer: A

Question # 5
Answer: B