CT-GenAI関連資料 & CT-GenAI練習問題集

ちなみに、JPNTest CT-GenAIの一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1yVXrQujlJhXUf3GxloJKNDv0OW0dizxE

JPNTestが提供したISQIのCT-GenAI「ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0」試験問題と解答が真実の試験の練習問題と解答は最高の相似性があり、一年の無料オンラインの更新のサービスがあり、100%のパス率を保証して、もし試験に合格しないと、弊社は全額で返金いたします。

ISQI CT-GenAI Exam Syllabus Topics:

Section	Weight	Objectives
Managing Risks of Generative AI in Software Testing	25%	- Validation, verification, and mitigation strategies - Hallucinations, bias, inaccuracy, and consistency risks - Data privacy, security, and compliance concerns
LLM-Powered Test Infrastructure	10%	- AI agents and integration with test tools - RAG, fine-tuning, and model adaptation - Architecture and deployment considerations
Deploying and Integrating GenAI in Test Organisations	15%	- Measuring value and continuous improvement - Strategy, governance, and adoption roadmap - Roles, skills, and team readiness
Introduction to Generative AI for Software Testing	15%	- Use cases across the testing lifecycle - Capabilities and limitations relevant to testing - Core concepts: Generative AI, LLMs, foundation models
Prompt Engineering for Effective Software Testing	35%	- Prompt patterns for test design, data generation, automation - Principles and structure of effective prompts - Iterative refinement and evaluation of prompts

>> CT-GenAI関連資料 <<

CT-GenAI試験の準備方法｜高品質なCT-GenAI関連資料試験｜有効的なISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0練習問題集

私たち全員が知っているように、試験の準備プロセスは非常に面倒で時間がかかります。 CT-GenAI試験の準備のために他のことをするために時間を割く必要があり、多くの重要なことが遅れました。この問題に直面した場合は、CT-GenAIの実際の試験を選択してください。教材を使用すると、試験に参加できるのは準備に約20〜30時間かかる場合のみです。残りの時間は、やりたいことを何でもできます。これにより、レビューのプレッシャーを完全に軽減できます。

ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 認定 CT-GenAI 試験問題 (Q14-Q19):

質問 # 14
Which statement about fine-tuning for test tasks is INCORRECT?

A. It enhances relevance to organizational terminology and formats
B. It can be applied to smaller SLMs to improve task performance with lower compute
C. It adapts a pre-trained model to a domain using task-specific data
D. It replaces the model's general knowledge entirely and prevents overfitting

正解：D

解説：
The statement that fine-tuning "replaces the model's general knowledge entirely" isincorrect. Fine-tuning is a process of "incremental learning" where a pre-trained model (which already possesses vast general knowledge) is further trained on a smaller, domain-specific dataset-such as an organization's internal API documentation or historical test scripts. The goal is to adjust the model's internal weights so that it becomes more proficient in a specific area (Option A) and adheres better to local terminology and formatting standards (Option C). It doesnoterase the foundational language capabilities of the model. Furthermore, fine-tuning is a common strategy for Small Language Models (SLMs) to allow them to punch above their weight class in specific tasks while remaining computationally efficient (Option D). However, if done poorly, fine-tuning can actuallycauseoverfitting (where the model becomes too rigid and loses its ability to generalize), rather than preventing it. Therefore, fine-tuning should be viewed as a "specialization" layer rather than a total replacement of the model's base intelligence.

質問 # 15
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?

A. Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
B. Include references to version control systems like Git in the constraints.
C. Add a step to review the change log for syntax errors before analysis.
D. Specify that the role is a test architect specializing in CI/CD pipelines.

正解：A

質問 # 16
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?

A. 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.
B. 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.
C. 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.
D. 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.

正解：D

解説：
Option A is the superior choice because it strictly adheres to thestructured prompting patternrecommended in the CT-GenAI syllabus. This pattern divides the prompt into six distinct components:Role, Context, Instruction, Input Data, Constraints, and Output Format.By specifying theRole(Senior Test Automation Engineer), the model accesses relevant technical knowledge. TheInstructionis specific about using pytest and the requests library, and it explicitly lists both positive and negative scenarios. Most importantly, the Constraintssection provides the necessary "guardrails" for the code structure, such as the use of fixtures and clear assertions. Options B, C, and D are increasingly vague and fail to provide the model with the necessary technical boundaries to produce "production-ready" testware. Structured prompting reduces the "probabilistic drift" of the model, ensuring the output is not just functional code, but a script that follows industry-standard testing patterns (like modularity and clean naming conventions), making it directly usable within a CI/CD pipeline.

質問 # 17
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

A. Ability to trigger automated actions beyond conversation
B. Ability to respond to prompts without explicit user instructions
C. Use of a conversational tone and improved response personalization
D. Reliance on predefined templates to generate short, factual answers

正解：A

質問 # 18
A prompt begins: "You are a senior test manager responsible for risk-based test planning on a payments platform." Which component is this?

A. Context
B. Role
C. Instruction
D. Constraints

正解：B

解説：
In structured prompt engineering, theRolecomponent (also known as a Persona) is used to set the perspective, expertise, and tone of the LLM's response. By assigning the role of a "senior test manager," the tester instructs the model to adopt the specific domain knowledge, vocabulary, and professional standards associated with that position. This technique is highly effective because LLMs are trained on vast datasets containing diverse professional documents; invoking a specific persona helps the model narrow its "latent space" to retrieve information relevant to that specific field. For instance, a senior test manager persona will prioritize risk management, resource allocation, and high-level strategy, whereas a "junior developer" persona might focus more on syntax and local unit tests. WhileContext(Option B) provides the background of the project andInstruction(Option A) defines the specific task to be performed, theRoleserves as the foundation for how those instructions are interpreted. This ensures the generated testware aligns with the expected professional seniority and organizational maturity required for high-stakes environments like a payments platform.

質問 # 19
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CT-GenAI練習問題集: https://www.jpntest.com/shiken/CT-GenAI-mondaishu

P.S. JPNTestがGoogle Driveで共有している無料かつ新しいCT-GenAIダンプ：https://drive.google.com/open?id=1yVXrQujlJhXUf3GxloJKNDv0OW0dizxE