iSQI CT-GenAI Practice Exam Questions

iSQI CT-GenAI Sample Question Answers

Question # 1

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.

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Question # 2

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 

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Question # 3

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 

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Question # 4

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 

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Question # 5

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 

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Question # 6

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 

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Question # 7

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 

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Question # 8

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 

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Question # 9

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 

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Question # 10

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 

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