Pass CT-GenAI Exam – Real Test Engine PDF with 42 Questions [Q22-Q45]

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Pass CT-GenAI Exam – Real Test Engine PDF with 42 Questions

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ISQI CT-GenAI Exam Syllabus Topics:

Section	Weight	Objectives
Tools for Testing Generative AI	20%	– Using Tools for Common Testing Activities 1. Simulation and monitoring tools 2. Security testing tools 3. Prompt testing tools 4. Model evaluation tools – Testing Tools Overview 1. Categories of GenAI testing tools 2. Selecting appropriate tools for specific testing needs
Risks and Testing Challenges for Generative AI	30%	– Testing Challenges for Generative AI 1. Complexity of the AI component 2. Test oracle problem 3. Regulatory and compliance considerations 4. Subjectivity of quality assessment 5. Ethical testing concerns 6. Coverage challenges 7. Non-deterministic output behavior – Quality Risks Specific to Generative AI 1. Inconsistent responses across runs 2. Dependency on external components 3. Offensive, harmful, or biased content 4. Inappropriate output for the context 5. Incorrect or fabricated outputs (hallucinations) 6. Input sensitivity (prompt brittleness)
Fundamentals of Generative AI	20%	– AI Development Lifecycle 1. Model training and fine-tuning 2. Evaluation 3. Data collection, preparation, and curation 4. Deployment and monitoring – AI Terminology 1. Transformer architecture 2. Large Language Models (LLMs) 3. Generative AI (GenAI) 4. Artificial Intelligence (AI) 5. Tokens and prompts 6. Deep Learning 7. Machine Learning (ML) – Generative AI Concepts 1. AI model behavior 2. Training data and context windows 3. Alignment and guardrails 4. Model types (Base, Instruction-tuned, RAG) 5. Hallucinations 6. Emergent capabilities and limitations
Testing Activities for Generative AI	30%	– Requirements-Based Testing 1. Functional requirements for AI-based systems 2. AI-related quality requirements 3. Non-functional requirements for AI-based systems – Traceability and Documentation 1. Documentation requirements for AI testing 2. Test coverage of AI model components – Prompt-Based Testing 1. Test data creation with GenAI 2. Prompt engineering basics 3. Test case design using prompts – Model and Output Evaluation 1. Checkpoint testing 2. Output correctness and quality assessment 3. Automated evaluation methods 4. Human evaluation methods 5. Metamorphic testing

NO.22 Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

The number of tokens processed directly determines the carbon intensity of each query

The location of the data center determines model bias and accuracy levels

The duration of user sessions primarily affects latency but not power efficiency

The type of cloud platform affects processing speed but not total energy draw

NO.23 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

NO.24 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.

NO.25 What is a key data-related aspect when defining a GenAI strategy for testing?

Neglect legacy data sources as they provide limited immediate relevance to testing tasks

Prioritize accurate and relevant input data secured through defined quality procedures

Aggregate data from all available organizational repositories without filtration

Use only auto-generated synthetic data to avoid dependency on enterprise repositories

NO.26 Which of the following is NOT a valid form of LLM-driven test data generation?

Creating production database backups

Setting boundary values

Creating combinatorial data (e.g., pairwise)

Generating synthetic datasets

NO.27 A prompt begins: “You are a senior test manager responsible for risk-based test planning on a payments platform.” Which component is this?

Instruction

Context

Role

Constraints

NO.28 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?

Generate prioritized conditions in one shot -> verify coverage

Generate conditions -> prioritize by risk -> map to requirements to find gaps

Detect requirement defects -> generate conditions -> prioritize

Prioritize requirements -> generate conditions -> review defects

NO.29 Which statement BEST describes vision-language models (VLMs)?

VLMs are a subset of multimodal LLMs integrating visual and textual information.

VLMs are unrelated to multimodal LLMs and focus only on UI automation.

VLMs are a superset of multimodal LLMs.

VLMs process audio and video but not images.

NO.30 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.

i, ii, iii

i, iii, v

i, ii, iii (Duplicate entry in original source)

ii, iii, iv

NO.31 Which statement BEST contrasts interaction style and scope?

Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.

Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing

Chatbots require API integration; LLM apps do not.

Both are identical aside from UI theme.

NO.32 Which concept refers to breaking text into smaller units for processing by LLMs?

Transformer

Embeddings

Context Window

Tokenization

NO.33 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

NO.34 What defines a prompt pattern in the context of structured GenAI capability building?

Treating prompts as access credentials or compliance records rather than functional templates

Maintaining static documentation repositories without real-time prompt standardization processes

Applying a reusable and structured template that guides GenAI models toward consistent outputs

Using ad hoc prompts without reference to previously proven structures or examples

NO.35 Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

It dynamically generates test insights using contextual information

It produces scripted conversational responses similar to traditional bots

It focuses primarily on visual dashboards and user navigation features

It provides fixed responses from predefined rule sets and scripts

NO.36 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.

NO.37 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

NO.38 How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

Replacing existing test coverage validation with automated summary reports generated by AI

Transitioning from manual execution to complete automation with no human oversight

Moving from black-box exploratory testing toward exclusively performing code-based white-box checks

Shifting from test execution toward reviewing, refining, and validating AI-generated testware

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Pass CT-GenAI Exam – Real Test Engine PDF with 42 Questions [Q22-Q45]

ISQI CT-GenAI Exam Syllabus Topics:

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