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Exam Code: CT-GenAI
Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
Certification Provider: ISQI
Corresponding Certification: AI Testing
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  • Total Questions: 42
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  • Total Questions: 42
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Reputed materials earn approbation slowly and lose it instantly. PremiumVCEDump protects its standing the durable way: accurate 42 practice questions for the ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 exam, reviewed content, and service that answers with patience.

ISQI CT-GenAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Risks and Testing Challenges for Generative AI30%- Quality Risks Specific to Generative AI
  • 1. Input sensitivity (prompt brittleness)
  • 2. Inappropriate output for the context
  • 3. Offensive, harmful, or biased content
  • 4. Dependency on external components
  • 5. Incorrect or fabricated outputs (hallucinations)
  • 6. Inconsistent responses across runs
- Testing Challenges for Generative AI
  • 1. Non-deterministic output behavior
  • 2. Subjectivity of quality assessment
  • 3. Ethical testing concerns
  • 4. Test oracle problem
  • 5. Regulatory and compliance considerations
  • 6. Complexity of the AI component
  • 7. Coverage challenges
Topic 2: Testing Activities for Generative AI30%- Model and Output Evaluation
  • 1. Checkpoint testing
  • 2. Metamorphic testing
  • 3. Human evaluation methods
  • 4. Automated evaluation methods
  • 5. Output correctness and quality assessment
- Requirements-Based Testing
  • 1. AI-related quality requirements
  • 2. Functional requirements for AI-based systems
  • 3. Non-functional requirements for AI-based systems
- Prompt-Based Testing
  • 1. Test data creation with GenAI
  • 2. Test case design using prompts
  • 3. Prompt engineering basics
- Traceability and Documentation
  • 1. Test coverage of AI model components
  • 2. Documentation requirements for AI testing
Topic 3: Fundamentals of Generative AI20%- Generative AI Concepts
  • 1. Model types (Base, Instruction-tuned, RAG)
  • 2. Hallucinations
  • 3. AI model behavior
  • 4. Emergent capabilities and limitations
  • 5. Alignment and guardrails
  • 6. Training data and context windows
- AI Terminology
  • 1. Machine Learning (ML)
  • 2. Tokens and prompts
  • 3. Large Language Models (LLMs)
  • 4. Generative AI (GenAI)
  • 5. Artificial Intelligence (AI)
  • 6. Transformer architecture
  • 7. Deep Learning
- AI Development Lifecycle
  • 1. Model training and fine-tuning
  • 2. Deployment and monitoring
  • 3. Data collection, preparation, and curation
  • 4. Evaluation
Topic 4: Tools for Testing Generative AI20%- Using Tools for Common Testing Activities
  • 1. Simulation and monitoring tools
  • 2. Prompt testing tools
  • 3. Security testing tools
  • 4. Model evaluation tools
- Testing Tools Overview
  • 1. Categories of GenAI testing tools
  • 2. Selecting appropriate tools for specific testing needs

CT-GenAI Exam FAQ — Products and Policies

The ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 exam contains 40 to complete within 60 minutes. Timed simulation in the PremiumVCEDump Desktop Test Engine teaches that rhythm before exam day does.

Three versions of the same study materials: a printable, expert-prepared PDF with free demo download and instant access; a Desktop Test Engine for Windows with customizable simulation and timed modes that works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. All 42 practice questions are identical across versions. Every purchase includes 365 days of free updates, a 50% renewal discount afterward, and unlimited computer installations.

The official ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 outline defines 4 domains. The leading three are Tools for Testing Generative AI (20%), Testing Activities for Generative AI (30%), and Fundamentals of Generative AI (20%). The full list is in the topics section above, and PremiumVCEDump's 42 practice questions cover each one.

The ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 exam requires 65% to pass, and registration costs 250 EUR (+ VAT). A failed attempt means paying the same fee again — a readiness check with the PremiumVCEDump engines before booking is the cheaper option.

The CT-GenAI exam is a ISQI certification exam validating the ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 syllabus shown above. It belongs to these credential paths: ISTQB CTFL (Foundation Level Tester). Candidates in 2026 keep up with it using PremiumVCEDump's 42 practice questions, available as a printable PDF, a Windows Desktop Test Engine, and an Online Test Engine — three versions of the same materials.

PremiumVCEDump issues a full refund if you take the ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 exam within 60 days of purchase and do not pass. The policy does not apply if the exam is taken within 3 days of purchase, if the exam was never actually taken, or to free materials or expired orders, and the candidate name must match the payer name. Submit a scan of your enrollment slip and the official Score Report PDF within 2 days of the exam; claims are processed within 7 days. Alternatively, exchange for two free exam products of equal value and keep your original update service. Orders arrive by email within 1 minute — contact support if nothing arrives within 2 hours.

ISTQB CTFL (Foundation Level Tester) certification is recommended but not required Since requirements change over time, confirm the latest on the official ISQI exam page before scheduling.

ISQI recommends these training resources for the ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 exam:

    Follow the training with steady self-testing — the 42 practice questions from PremiumVCEDump help you apply theory and fill any gaps the training leaves.

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      ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:

      Which statement BEST contrasts interaction style and scope?

      • A. Both are identical aside from UI theme.
      • B. Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing
      • C. Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.
      • D. Chatbots require API integration; LLM apps do not.
      Reveal Solution  Discussion  0

      Correct Answer: C  🗳️

      Explanation: Only visible for PremiumVCEDump members. You can sign-up / login (it's free).

      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

      • A. iv -> iii -> i -> ii
      • B. iv -> i -> iii -> ii
      • C. iii -> iv -> i -> ii
      • D. i -> iv -> iii -> ii
      Reveal Solution  Discussion  0

      Correct Answer: B  🗳️

      Explanation: Only visible for PremiumVCEDump members. You can sign-up / login (it's free).

      How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

      • A. Replacing existing test coverage validation with automated summary reports generated by AI
      • B. Shifting from test execution toward reviewing, refining, and validating AI-generated testware
      • C. Moving from black-box exploratory testing toward exclusively performing code-based white-box checks
      • D. Transitioning from manual execution to complete automation with no human oversight
      Reveal Solution  Discussion  0

      Correct Answer: B  🗳️

      Explanation: Only visible for PremiumVCEDump members. You can sign-up / login (it's free).

      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?

      • A. Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.
      • B. Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.
      • C. Emphasize constraints that focus on deviations that could impact release timelines or quality gates.
      • D. Specify that the role is a test analyst with expertise in predictive analytics and risk management.
      Reveal Solution  Discussion  0

      Correct Answer: A  🗳️

      Explanation: Only visible for PremiumVCEDump members. You can sign-up / login (it's free).

      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. Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
      • C. Diversity for anomaly identification and precision for partitions
      • D. Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions
      Reveal Solution  Discussion  0

      Correct Answer: A  🗳️

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