一発合格問題CT-GenAI厳選問題集

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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
Deploying and Integrating GenAI in Test Organisations	15%	- Strategy, governance, and adoption roadmap - Roles, skills, and team readiness - Measuring value and continuous improvement
Prompt Engineering for Effective Software Testing	35%	- Iterative refinement and evaluation of prompts - Prompt patterns for test design, data generation, automation - Principles and structure of effective prompts
LLM-Powered Test Infrastructure	10%	- AI agents and integration with test tools - RAG, fine-tuning, and model adaptation - Architecture and deployment considerations
Introduction to Generative AI for Software Testing	15%	- Capabilities and limitations relevant to testing - Core concepts: Generative AI, LLMs, foundation models - Use cases across the testing lifecycle

>> CT-GenAI技術試験 <<

ISQI CT-GenAI日本語版対策ガイド & CT-GenAI受験方法

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 認定 CT-GenAI 試験問題 (Q37-Q42):

質問 # 37
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

A. Using GenAI without regulatory compliance can lead to legal exposure
B. Strict GDPR compliance eliminates all privacy risk
C. Some GenAI tools may store/process data without explicit consent
D. GenAI outputs can accidentally reveal sensitive information present in inputs

正解：B

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 # 38
What BEST protects sensitive test data at rest and in transit?

A. Use public file shares with read-only links
B. Rely on obfuscation instead of encryption
C. Enforce role-based access controls
D. Disable TLS and rely on VPN only

正解：C

解説：
Data security is a paramount concern when using GenAI in testing, as test environments often contain sensitive business logic or PII (Personally Identifiable Information). To protect this data "at rest" (stored in databases or vector stores) and "in transit" (being sent to the LLM), a combination of technical controls is required.Role-Based Access Control (RBAC)is a fundamental security pillar that ensures only authorized individuals or services can access specific datasets or trigger GenAI workflows. This prevents unauthorized users from feeding sensitive enterprise data into public AI models. While encryption (omitted in Option A as an alternative to obfuscation) and TLS (falsely suggested to be disabled in Option C) are essential technical layers for protecting data in transit, RBAC provides the organizational "gatekeeping" necessary to manage who can interact with the AI system. In a professional GenAI strategy, testers must ensure that the tools they use adhere to strict access policies, ensuring that the "Input Data" used for prompting remains within the secured organizational boundary and is not leaked to unauthorized entities or public training sets.

質問 # 39
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 # 40
What is a key data-related aspect when defining a GenAI strategy for testing?

A. Neglect legacy data sources as they provide limited immediate relevance to testing tasks
B. Aggregate data from all available organizational repositories without filtration
C. Prioritize accurate and relevant input data secured through defined quality procedures
D. Use only auto-generated synthetic data to avoid dependency on enterprise repositories

正解：C

解説：
A successful Generative AI strategy for testing is heavily dependent on the quality of the data used for grounding (RAG) and prompting. The principle of "Garbage In, Garbage Out" is magnified with LLMs; therefore, a key strategic pillar is the prioritization of accurate, relevant, and high-quality input data. This involves establishing defined quality procedures to ensure that the requirements, codebases, and historical defect logs fed into the model are "clean" and representative of the current system state. Strategy must avoid the "unfiltered" approach (Option C), as including contradictory or obsolete data can lead to hallucinations or irrelevant test cases. While synthetic data (Option D) is a powerful tool for privacy, it cannot entirely replace the nuanced reality found in secured enterprise data. Furthermore, legacy data (Option A) often contains valuable insights for regression testing. Consequently, the strategy should focus on building a robust data pipeline that ensures only verified, contextually appropriate information is utilized, thereby increasing the reliability of AI-generated testware and ensuring it aligns with the organization's quality standards.

質問 # 41
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

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

正解：B

質問 # 42
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CT-GenAI日本語版対策ガイド: https://www.mogiexam.com/CT-GenAI-exam.html

P.S.MogiExamがGoogle Driveで共有している無料の2026 ISQI CT-GenAIダンプ：https://drive.google.com/open?id=1Yoblzvh682FP88RkdRB4BMTJr7HwqlWy