ISQI CT-GenAI資格認定試験 |最初の試行で簡単に勉強して試験に合格する & CT-GenAI: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0

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ISQI CT-GenAI Exam Syllabus Topics:

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

>> CT-GenAI資格認定試験 <<

権威のあるCT-GenAI資格認定試験 & 合格スムーズCT-GenAI受験方法 | ユニークなCT-GenAI再テスト

ISQIの認定試験は現在とても人気がある試験ですね。この重要な認証資格をもうすでに手に入れましたか。例えば、もう既にCT-GenAI認定試験を受験したのですか。もしまだ受験していないなら、はやく行動する必要がありますよ。こんなに大切な資格を取らなくてはいけないです。ここで言いたいのは、どのようにすれば効率的にCT-GenAI認定試験の準備をして一回で試験に合格できるのかということです。

ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 認定 CT-GenAI 試験問題 (Q22-Q27):

質問 # 22
Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

正解：D

解説：
The environmental impact and sustainability of AI are increasingly important considerations in software engineering. The overall energy consumption of an LLM during inference (when the model is actually being used by a tester) is most directly influenced by thenumber of tokens processed. Every token generated or analyzed requires a massive amount of floating-point operations within the GPU clusters of a data center.
Therefore, the "length" of the input prompt and the "length" of the AI's response are the primary drivers of the power draw and, consequently, the carbon intensity of the query. This is a crucial concept for "Green AI" initiatives in testing; more efficient prompting-such as avoiding unnecessarily verbose context or limiting output lengths-can lead to more sustainable testing practices. While data center location (Option B) affects thetypeof energy used (renewable vs. fossil fuel), it does not determine the model's accuracy. Similarly, while cloud platforms (Option D) and session durations (Option C) play roles in operational logistics, the mathematical workload tied to token count remains the fundamental unit of energy expenditure in Generative AI.


質問 # 23
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

正解：D

解説：
While a basic chatbot is primarily designed for textual interaction and information retrieval, anLLM- powered agent(or AI Agent) is characterized by itsagency-the ability to use tools and trigger actions in the external world. In a software testing context, an agent does not just "talk" about testing; it can actually perform testing tasks. For example, an agent could be given the goal to "verify the login module," and it would independently decide to call an API, generate a test script, execute it against a test environment, and then analyze the results to report a bug in Jira. This ability totrigger automated actions(Option C) through
"function calling" or tool integration is what makes agents far more powerful than simple conversational interfaces (Option D). Agents can reason about "how" to achieve a goal, selecting the appropriate tools (like Selenium, Postman, or specialized internal utilities) to complete the task. This moves the AI from being a passive advisor to an active participant in the test automation ecosystem, requiring testers to focus more on goal definition and result validation.


質問 # 24
Which option BEST differentiates the three prompting techniques?

正解：A

解説：
Differentiating between prompting techniques is essential for a tester to select the right tool for the task.Few- shot promptingis characterized by providing the model with a few examples of inputs and desired outputs, allowing it to learn the pattern and format.Prompt Chaininginvolves breaking a complex task into a sequence of smaller, interconnected prompts, where the output of one step becomes the input for the next (e.g., first extract requirements, then generate test cases from those requirements).Meta-promptingis a more advanced technique where the user asks the LLM to help design, write, or refine the prompt itself-essentially using the AI as a "prompt engineer" to optimize the instructions. Option D correctly identifies these core characteristics.
Options A, B, and C contain fundamental mischaracterizations: for instance, Few-shotrequiresexamples (contradicting A), and Chaining is theoppositeof a single prompt (contradicting A). Mastering these distinctions allows testers to move from simple "chatting" to sophisticated AI orchestration that can handle complex, multi-stage testing workflows with high reliability.


質問 # 25
Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?

正解：D

解説：
As Generative AI becomes integrated into the software testing lifecycle, the role of the tester shifts from manual authoring to the "orchestration" of AI models. Mastering prompt engineering is the primary competency required to effectively steer LLMs. Prompt engineering involves the deliberate design of inputs- incorporating roles, context, instructions, and constraints-to elicit the most accurate and "on-policy" outputs from the model. In a testing context, "on-policy" refers to testware that adheres to organizational standards, security protocols, and specific project requirements. While technical skills like network configuration or low- level programming (Options B, C, and D) are valuable in specific engineering domains, they do not directly influence the communicative interface between the human and the AI. A tester proficient in prompt engineering can utilize techniques like "Chain-of-Thought" or "Few-shot prompting" to ensure the LLM understands the nuances of a test plan, thereby reducing hallucinations and ensuring the generated test cases are actionable, relevant, and compliant with the project's quality gates.


質問 # 26
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

正解：D

解説：
The statement that "Strict GDPR compliance eliminates all privacy risk" isincorrectbecause compliance is a legal and procedural framework, not a foolproof technical shield against all possible risks. Even within a GDPR-compliant environment, risks such as "model inversion" attacks, accidental data leakage through
"membership inference," or the unintentional generation of Sensitive Personally Identifiable Information (SPII) can still occur. Data privacy in GenAI is complex because LLMs function by processing and sometimes retaining patterns from the data they are fed. As noted in the CT-GenAI syllabus, some tools may process data in ways that are not fully transparent (Option A), and outputs can inadvertently include snippets of sensitive data used during the prompting or training phase (Option B). Furthermore, failing to adhere to regulations like GDPR or the EU AI Act certainly leads to legal and financial exposure (Option D). Therefore, while compliance frameworks significantly mitigate risk, they do not "eliminate" it; a robust GenAI strategy requires ongoing technical controls, data masking, and human oversight to manage residual privacy threats effectively.


質問 # 27
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