[Aug 08, 2026] Reliable CT-GenAI Exam Tips Test Pdf Exam Material [Q23-Q40]

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[Aug 08, 2026] Reliable CT-GenAI Exam Tips Test Pdf Exam Material

New 2026 CT-GenAI Test Tutorial (Updated 42 Questions)

ISQI CT-GenAI Exam Syllabus Topics:

Section	Weight	Objectives
LLM-Powered Test Infrastructure	10%	– Architecture and deployment considerations – AI agents and integration with test tools – RAG, fine-tuning, and model adaptation
Deploying and Integrating GenAI in Test Organisations	15%	– Measuring value and continuous improvement – Roles, skills, and team readiness – Strategy, governance, and adoption roadmap
Managing Risks of Generative AI in Software Testing	25%	– Validation, verification, and mitigation strategies – Data privacy, security, and compliance concerns – Hallucinations, bias, inaccuracy, and consistency risks
Prompt Engineering for Effective Software Testing	35%	– Prompt patterns for test design, data generation, automation – Principles and structure of effective prompts – Iterative refinement and evaluation of prompts
Introduction to Generative AI for Software Testing	15%	– Core concepts: Generative AI, LLMs, foundation models – Use cases across the testing lifecycle – Capabilities and limitations relevant to testing

Q23. What are the three key phases in adopting GenAI in a test organization?

Discovery; initiation and usage definition; utilization and iteration

Prototype; pilot; decommission

Training; certification; outsourcing

Planning; execution; sign-off

Q24. Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization’s standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?

Prompt chaining

Few-shot prompting

Meta prompting

Zero-shot prompting

Q25. Which statement about fine-tuning for test tasks is INCORRECT?

It adapts a pre-trained model to a domain using task-specific data

It replaces the model’s general knowledge entirely and prevents overfitting

It enhances relevance to organizational terminology and formats

It can be applied to smaller SLMs to improve task performance with lower compute

Q26. Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

Select models with maximum vendor visibility and strong online presence to ensure reliability

Select open-source models prioritizing creativity over compliance or performance consistency

Select broad-coverage models offering diverse functionalities for various test scenarios

Select LLMs aligned to measurable test outcomes, compatible with current infrastructure

Q27. What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

Reliance on predefined templates to generate short, factual answers

Ability to respond to prompts without explicit user instructions

Ability to trigger automated actions beyond conversation

Use of a conversational tone and improved response personalization

Q28. Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

ISO/IEC 42001:2023

NIST AI RMF 1.0

ISO/IEC 23053:2022

EU AI Act

Q29. An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?

No defect; this is acceptable

Reasoning error in risk calculation logic

Hallucination

Dataset bias toward UI features

Q30. A team notices vague, inconsistent LLM outputs for the same story for two different prompts. Which technique BEST helps choose the stronger wording among two prompt versions using predefined metrics?

A/B testing of prompts

Iterative prompt modification

Output analysis

Integrating user feedback

Q31. What does an embedding represent in an LLM?

Tokens grouped into context windows

Numerical vectors capturing semantic relationships

Logical rules for reasoning

A set of test cases for validation

Q32. An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?

Data poisoning

Malicious code generation

Data exfiltration

Request manipulation

Q33. Consider applying the meta-prompting technique to generate automated test scripts for API testing. You need to test a REST API endpoint that processes user registration with validation rules. Which one of the following prompts is BEST suited to this task?

Role: Act as a test automation engineer with API testing experience. | Context: You are verifying user registration that enforces field and format validation. | Instruction: Generate pytest scripts using requests for both positive (valid) and negative (invalid email, weak password, missing fields) cases. | Input Data: POST /api/register with validation rules for email and password length. | Constraints:
Include fixtures, clear assertions, and naming consistent with pytest. | Output Format: Return complete Python test files.

Role: Act as a test automation engineer. | Context: You are creating tests for a registration endpoint. | Instruction: Generate Python test scripts using pytest covering both valid and invalid inputs. | Input Data: POST /api/register with email and password. | Constraints: Follow pytest structure. | Output Format: Provide scripts.

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. | Output Format: Provide organized scripts.

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.

Q34. What BEST protects sensitive test data at rest and in transit?

Rely on obfuscation instead of encryption

Enforce role-based access controls

Disable TLS and rely on VPN only

Use public file shares with read-only links

Q35. Which setting can reduce variability by narrowing the sampling distribution during inference?

Increasing temperature

Increasing learning rate

Lowering temperature

Using a larger context window

Q36. What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

Ability to respond to prompts without explicit user instructions

Ability to trigger automated actions beyond conversation

Reliance on predefined templates to generate short, factual answers

Use of a conversational tone and improved response personalization

Q37. 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?

Specify that the role is a test architect specializing in CI/CD pipelines.

Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity

Include references to version control systems like Git in the constraints.

Add a step to review the change log for syntax errors before analysis.

Q38. You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?
i. Retrieve semantically similar specification chunks from the vector database ii. Feed both retrieved datasets as context for the LLM to generate new test cases iii. Retrieve relevant historical cases from the relational database iv. Submit a focused query describing the new test requirement

iv -> iii -> i -> ii

iv -> i -> iii -> ii

iii -> iv -> i -> ii

i -> iv -> iii -> ii

Q39. 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?

Emphasize constraints that focus on deviations that could impact release timelines or quality gates.

Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.

Specify that the role is a test analyst with expertise in predictive analytics and risk management.

Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.