iSQI CT-GenAI Dumps

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Exam Code CT-GenAI
Exam Name ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
Update Date 18 Jul, 2026
Total Questions 40 Questions Answers With Explanation
$79

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Question # 1

Who typically defines the system prompt in a testing workflow? 

A. A tester configuring the assistant 
B. End user during normal chat use 
C. CI server automatically without human input 
D. Product owner in user stories only 

Question # 2

An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above apayment failure. What defect does this MOST LIKELY show?

A. No defect; this is acceptable 
B. Reasoning error in risk calculation logic 
C. Hallucination 
D. Dataset bias toward UI features 

Question # 3

Which concept refers to breaking text into smaller units for processing by LLMs? 

A. Transformer 
B. Embeddings 
C. Context Window 
D. Tokenization 

Question # 4

Which setting can reduce variability by narrowing the sampling distribution during inference?

A. Increasing temperature 
B. Increasing learning rate 
C. Lowering temperature 
D. Using a larger context window 

Question # 5

Which statement BEST describes vision-language models (VLMs)? 

A. VLMs are a subset of multimodal LLMs integrating visual and textual information. 
B. VLMs are unrelated to multimodal LLMs and focus only on UI automation. 
C. VLMs are a superset of multimodal LLMs. 
D. VLMs process audio and video but not images. 

Question # 6

Which standard specifies requirements for managing AI systems within an organization,supporting consistent GenAI use in testing?

A. ISO/IEC 42001:2023 
B. NIST AI RMF 1.0 
C. ISO/IEC 23053:2022 
D. EU AI Act 

Question # 7

Consider applying the meta-prompting technique to generate automated test scripts for APItesting. You need to test a REST API endpoint that processes user registration withvalidation rules. Which one of the following prompts is BEST suited to this task?

A. Role: Act as a test automation engineer with API testing experience. | Context: You areverifying user registration that enforces field and format validation. | Instruction: Generatepytest scripts using requests for both positive (valid) and negative (invalid email, weakpassword, missing fields) cases. | Input Data: POST /api/register with validation rules foremail and password length. | Constraints: Include fixtures, clear assertions, and namingconsistent with pytest. | Output Format: Return complete Python test files.
B. Role: Act as a test automation engineer. | Context: You are creating tests for aregistration endpoint. | Instruction: Generate Python test scripts using pytest covering bothvalid and invalid inputs. | Input Data: POST /api/register with email and password. |Constraints: Follow pytest structure. | Output Format: Provide scripts.
C. Role: Act as an automation tester. | Context: You are validating an API endpoint. |Instruction: Generate Python test scripts that send POST requests and validate responses.| Input Data: User credentials. | Constraints: Include basic scenarios with asserts. | OutputFormat: Provide organized scripts.
D. Role: Act as a software engineer. | Context: You are testing registration logic. |Instruction: Create Python scripts to verify endpoint behavior. | Input Data: POST/api/register with test users. | Constraints: Add checks for status codes. | Output Format:Deliver functional scripts.

Question # 8

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

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

Question # 9

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

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

Question # 10

What is a hallucination in LLM outputs? 

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

Question # 11

Which technique MOST directly reduces hallucinations by grounding the model in project realities? 

A. Provide detailed context 
B. Randomize prompts each run 
C. Rely on generic examples only 
D. Use longer temperature settings 

Question # 12

Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing? 

A. It dynamically generates test insights using contextual information 
B. It produces scripted conversational responses similar to traditional bots 
C. It focuses primarily on visual dashboards and user navigation features 
D. It provides fixed responses from predefined rule sets and scripts 

Question # 13

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 open-source models prioritizing creativity over compliance or performance consistency 
C. Select broad-coverage models offering diverse functionalities for various test scenarios 
D. Select LLMs aligned to measurable test outcomes, compatible with current infrastructure 

Question # 14

What defines a prompt pattern in the context of structured GenAI capability building? 

A. Treating prompts as access credentials or compliance records rather than functional templates
B. Maintaining static documentation repositories without real-time prompt standardization processes
C. Applying a reusable and structured template that guides GenAI models toward consistent outputs
D. Using ad hoc prompts without reference to previously proven structures or examples 

Question # 15

What does an embedding represent in an LLM? 

A. Tokens grouped into context windows 
B. Numerical vectors capturing semantic relationships 
C. Logical rules for reasoning 
D. A set of test cases for validation 

Question # 16

What distinguishes an LLM-powered agent from a basic AI chatbot in test processes? 

A. Reliance on predefined templates to generate short, factual answers 
B. Ability to respond to prompts without explicit user instructions 
C. Ability to trigger automated actions beyond conversation 
D. Use of a conversational tone and improved response personalization 

Question # 17

A team notices vague, inconsistent LLM outputs for the same story for two differentprompts. Which technique BEST helps choose the stronger wording among two promptversions using predefined metrics?

A. A/B testing of prompts 
B. Iterative prompt modification 
C. Output analysis 
D. Integrating user feedback 

Question # 18

When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern? 

A. Maximizing scalability by deploying larger cloud-based LLM clusters 
B. Maintaining data privacy and minimizing security risks from external services 
C. Achieving faster responses by reducing model checkpoints and updates 
D. Focusing primarily on user experience improvements and response formatting 

Question # 19

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

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

Question # 20

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 risksand 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. Specify that the role is a test analyst with expertise in predictive analytics and risk management.
D. Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.