ISQI Certified CT-GenAI Dumps Questions Valid CT-GenAI Materials [Q18-Q32]

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ISQI Certified CT-GenAI  Dumps Questions Valid CT-GenAI Materials

Current CT-GenAI Exam Dumps [2026] Complete ISQI Exam Smoothly

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. Model evaluation tools
  • 3. Security testing tools
  • 4. Prompt testing tools

– Testing Tools Overview

  • 1. Categories of GenAI testing tools
  • 2. Selecting appropriate tools for specific testing needs
Fundamentals of Generative AI 20% – Generative AI Concepts

  • 1. Training data and context windows
  • 2. Alignment and guardrails
  • 3. AI model behavior
  • 4. Model types (Base, Instruction-tuned, RAG)
  • 5. Hallucinations
  • 6. Emergent capabilities and limitations

– AI Terminology

  • 1. Artificial Intelligence (AI)
  • 2. Machine Learning (ML)
  • 3. Generative AI (GenAI)
  • 4. Tokens and prompts
  • 5. Deep Learning
  • 6. Large Language Models (LLMs)
  • 7. Transformer architecture

– AI Development Lifecycle

  • 1. Data collection, preparation, and curation
  • 2. Evaluation
  • 3. Deployment and monitoring
  • 4. Model training and fine-tuning
Testing Activities for Generative AI 30% – Prompt-Based Testing

  • 1. Test case design using prompts
  • 2. Prompt engineering basics
  • 3. Test data creation with GenAI

– Requirements-Based Testing

  • 1. Non-functional requirements for AI-based systems
  • 2. AI-related quality requirements
  • 3. Functional requirements for AI-based systems

– Model and Output Evaluation

  • 1. Output correctness and quality assessment
  • 2. Checkpoint testing
  • 3. Automated evaluation methods
  • 4. Metamorphic testing
  • 5. Human evaluation methods

– Traceability and Documentation

  • 1. Test coverage of AI model components
  • 2. Documentation requirements for AI testing
Risks and Testing Challenges for Generative AI 30% – Quality Risks Specific to Generative AI

  • 1. Inappropriate output for the context
  • 2. Inconsistent responses across runs
  • 3. Offensive, harmful, or biased content
  • 4. Input sensitivity (prompt brittleness)
  • 5. Dependency on external components
  • 6. Incorrect or fabricated outputs (hallucinations)

– Testing Challenges for Generative AI

  • 1. Subjectivity of quality assessment
  • 2. Test oracle problem
  • 3. Ethical testing concerns
  • 4. Regulatory and compliance considerations
  • 5. Non-deterministic output behavior
  • 6. Coverage challenges
  • 7. Complexity of the AI component

 

NO.18 Which concept refers to breaking text into smaller units for processing by LLMs?

 
 
 
 

NO.19 Which setting can reduce variability by narrowing the sampling distribution during inference?

 
 
 
 

NO.20 A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

 
 
 
 

NO.21 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?

 
 
 
 

NO.22 Which statement BEST describes vision-language models (VLMs)?

 
 
 
 

NO.23 The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

 
 
 
 

NO.24 Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

 
 
 
 

NO.25 Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

 
 
 
 

NO.26 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?

 
 
 
 

NO.27 Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?

 
 
 
 

NO.28 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?

 
 
 
 

NO.29 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?

 
 
 
 

NO.30 Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

 
 
 
 

NO.31 What are the three key phases in adopting GenAI in a test organization?

 
 
 
 

NO.32 What does an embedding represent in an LLM?

 
 
 
 

CT-GenAI Premium PDF & Test Engine Files with 42 Questions & Answers: https://www.vcedumps.com/CT-GenAI-examcollection.html

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