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ISQI CT-GenAI Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
|
| Topic 2: Prompt Engineering for Testing | - Prompt design techniques
|
| Topic 3: Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
|
| Topic 4: Organizational Adoption and Governance | - Enterprise GenAI adoption
|
| Topic 5: Application of GenAI in Software Testing | - Practical use in testing workflows
|
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
Question #1
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
A. Ability to respond to prompts without explicit user instructions
B. Use of a conversational tone and improved response personalization
C. Ability to trigger automated actions beyond conversation
D. Reliance on predefined templates to generate short, factual answers
Question #2
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. Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions
C. Precision for anomaly identification and recall for coverage of valid/invalid partitions
D. Diversity for anomaly identification and precision for partitions
Question #3
An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?
A. Malicious code generation
B. Request manipulation
C. Data exfiltration
D. Data poisoning
Question #4
Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?
A. It produces scripted conversational responses similar to traditional bots
B. It focuses primarily on visual dashboards and user navigation features
C. It provides fixed responses from predefined rule sets and scripts
D. It dynamically generates test insights using contextual information
Question #5
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. i, ii, iii
B. i, ii, iii (Duplicate entry in original source)
C. i, iii, v
D. ii, iii, iv
Solutions:
| Question #1 Answer: C | Question #2 Answer: C | Question #3 Answer: C | Question #4 Answer: D | Question #5 Answer: A |
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