よくできたCT-GenAI資格認定 &資格試験におけるリーダーオファー &素晴らしいISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0

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ISQI CT-GenAI Exam Syllabus Topics:

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

>> CT-GenAI資格認定 <<

CT-GenAIテスト参考書、CT-GenAI最速合格

市場では、顧客の観点から判断するための未定の品質を備えたいくつかの実習用教材が市場に登場しています。 間違ったCT-GenAI練習教材を選択した場合、重大な間違いになります。 彼らの行動は厳密に倫理的ではなく、あなたにとって無責任ではありません。 進捗状況を確認し、CT-GenAIトレーニング資料の証明書を取得することは、当然のことながら、最新かつ最も正確な知識を備えた最も専門的な専門家によるものです。 ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0試験準備は市場の大部分を占めています。 次のようにいくつかの機能を知ってください。

ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 認定 CT-GenAI 試験問題 (Q10-Q15):

質問 # 10
When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?

正解：A

解説：
LLMOps(Large Language Model Operations) is the set of practices used to manage the lifecycle of LLMs in production. When an organization integrates an AI chatbot into its test processes, the primary operational concern ismaintaining data privacy and minimizing security risks, especially if using third-party APIs.
Unlike traditional software, LLMs are "black boxes" that process every piece of data sent to them. A core LLMOps responsibility is ensuring that any "Prompt Data" (code, requirements, or logs) is not used by the provider to train their public models and that the communication channels are fully secured. While scalability (Option A) and latency (Option C) are important technical metrics, they are secondary to the catastrophic legal and reputational risk of a data breach. LLMOps in a testing context involves implementing data masking tools, monitoring for "Prompt Injection" attacks, and managing the "Grounding" data in vector databases to ensure it remains current and protected. This ensures the AI remains a safe and reliable asset within the enterprise testing ecosystem, rather than a liability for the organization's intellectual property.


質問 # 11
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?

正解：C

解説：
This scenario describes a failure in the model's ability to apply logical weight to specific domain concepts, specifically in the context of Risk-Based Testing (RBT). When an LLM ranks a low-impact UI element (a tooltip) higher than a critical functional failure (payment processing), it demonstrates a "Reasoning error in risk calculation logic." While LLMs can follow formulas like $Risk = Likelihood \times Impact$, they may lack the deep semantic understanding of "Impact" within a specific business domain unless explicitly guided.
This is not necessarily a hallucination (Option C), as the model isn't necessarily inventing facts, but rather misapplying the logic of prioritization. It is also distinct from dataset bias (Option D), which would involve a systematic skewing across all outputs. In professional testing, this type of error highlights the necessity of
"human-in-the-loop" verification. Testers must review AI-generated prioritizations to ensure that the logical deductions align with the actual business risk and technical criticality of the features being tested.


質問 # 12
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?

正解：B


質問 # 13
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

正解：D


質問 # 14
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?

正解：C

解説：
Prompt Chainingis a technique where a complex task is decomposed into several smaller, sequential steps, where the output of one step serves as the context or input for the next. This is far more reliable than a "one- shot" approach (Option A) because it reduces the cognitive load on the LLM and allows for intermediate verification. In the scenario of test analysis, the most logical and effective chain begins by extracting discrete test conditionsfrom the raw requirements. Once these conditions are established, the next "link" in the chain is toprioritize them based on risk(impact and likelihood), which requires the model to reason specifically about the importance of each condition. The final step is tomap these prioritized conditions back to the original requirementsto identify any "coverage gaps." This systematic flow (Option B) mirrors the professional test analysis process defined in the ISTQB/CT-GenAI standards. By following this sequence, the tester ensures that the AI-generated output is logically derived and thorough, providing a clear "audit trail" from the initial requirement to the final prioritized test suite.


質問 # 15
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