真実的なCT-GenAI試験対策試験-試験の準備方法-効率的なCT-GenAI的中問題集

P.S. CertShiken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=1-Bn8hAVf6h6QlZQTpSxkrfxUDYbkrsH2

CT-GenAI試験のブレーンダンプを使用すると、あなたの成功は100％保証されます。 CT-GenAI学習教材は、最も正確なCT-GenAI試験問題を提供するだけでなく、3つの異なるバージョン（PDF、Soft、およびAPPバージョン）でも提供します。豊富な練習資料はお客様のさまざまなニーズに対応でき、これらのCT-GenAI模擬練習にはすべて、テストに合格するために知っておく必要がある新しい情報が含まれています。あなたの個人的な好みに応じてそれらを選択することができます。

ISQI CT-GenAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Testing Activities for Generative AI	30%	- Prompt-Based Testing 1. Test data creation with GenAI 2. Test case design using prompts 3. Prompt engineering basics - Requirements-Based Testing 1. Functional requirements for AI-based systems 2. AI-related quality requirements 3. Non-functional requirements for AI-based systems - Traceability and Documentation 1. Documentation requirements for AI testing 2. Test coverage of AI model components - Model and Output Evaluation 1. Automated evaluation methods 2. Human evaluation methods 3. Output correctness and quality assessment 4. Checkpoint testing 5. Metamorphic testing
Topic 2: Fundamentals of Generative AI	20%	- AI Development Lifecycle 1. Model training and fine-tuning 2. Deployment and monitoring 3. Data collection, preparation, and curation 4. Evaluation - Generative AI Concepts 1. Emergent capabilities and limitations 2. Hallucinations 3. AI model behavior 4. Alignment and guardrails 5. Model types (Base, Instruction-tuned, RAG) 6. Training data and context windows - AI Terminology 1. Artificial Intelligence (AI) 2. Machine Learning (ML) 3. Transformer architecture 4. Large Language Models (LLMs) 5. Generative AI (GenAI) 6. Deep Learning 7. Tokens and prompts
Topic 3: Risks and Testing Challenges for Generative AI	30%	- Testing Challenges for Generative AI 1. Regulatory and compliance considerations 2. Non-deterministic output behavior 3. Test oracle problem 4. Complexity of the AI component 5. Ethical testing concerns 6. Subjectivity of quality assessment 7. Coverage challenges - Quality Risks Specific to Generative AI 1. Inconsistent responses across runs 2. Dependency on external components 3. Offensive, harmful, or biased content 4. Input sensitivity (prompt brittleness) 5. Incorrect or fabricated outputs (hallucinations) 6. Inappropriate output for the context
Topic 4: Tools for Testing Generative AI	20%	- Testing Tools Overview 1. Selecting appropriate tools for specific testing needs 2. Categories of GenAI testing tools - Using Tools for Common Testing Activities 1. Security testing tools 2. Model evaluation tools 3. Prompt testing tools 4. Simulation and monitoring tools

>> CT-GenAI試験対策 <<

実用的-効率的なCT-GenAI試験対策試験-試験の準備方法CT-GenAI的中問題集

最短時間でCT-GenAI試験に合格すると、CertShikenすべての受験者の声になります。しかし、圧倒的な学習教材で最も価値のある情報を選択する方法は、すべての試験官にとって頭痛の種です。絶え間ない努力の後、CT-GenAI学習ガイドは誰もが期待するものです。当社の専門家は、コンテンツを簡素化し、お客様の重要なポイントを把握するだけでなく、CT-GenAI準備資料を簡単な言語に再コンパイルしました。レジャー学習体験と、今後のCT-GenAI 試験ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0合格できます。

ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 認定 CT-GenAI 試験問題 (Q25-Q30):

質問 # 25
Which of the following is NOT a valid form of LLM-driven test data generation?

A. Setting boundary values
B. Creating combinatorial data (e.g., pairwise)
C. Creating production database backups
D. Generating synthetic datasets

正解：C

解説：
Generative AI is exceptionally capable of creating structured and unstructured data, but its role is limited to
"generation" and "transformation," not infrastructure management or direct database administration. Creating production database backups (Option A) is a physical data management task involving the copying of actual stateful data from a server to storage; this is handled by database management systems (DBMS) and DevOps pipelines, not LLMs. Conversely, LLMs excel at the logic-based tasks listed in the other options. They can analyze requirements to identify and set boundary values (Option B) for input validation. They are also highly effective at creating combinatorial data (Option C), such as pairwise or all-combinations tables, by understanding the relationships between variables. Finally, one of the most powerful uses of GenAI in testing is generating synthetic datasets (Option D)-creating "fake" but realistically structured data that mimics production patterns without exposing Sensitive Personally Identifiable Information (SPII), thereby supporting privacy-compliant testing.

質問 # 26
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 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.
B. 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.
C. 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.
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 # 27
Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization's standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?

A. Prompt chaining
B. Few-shot prompting
C. Zero-shot prompting
D. Meta prompting

正解：B

解説：
Few-shot promptingis the technique of providing a few examples (exemplars) within the prompt to demonstrate the desired task and output format to the LLM. In this scenario, providing 10 existing, high- quality test cases acts as a "pattern" for the model to follow. This is significantly more effective than "Zero- shot prompting" (Option D), where the model is given a task without examples and may deviate from the specific organizational format required (e.g., specific JSON structures or assertion styles). While "Prompt chaining" (Option A) is useful for breaking down complex tasks into sub-tasks, the primary need here is pattern recognition and replication, which is the core strength of Few-shot learning. "Meta prompting" (Option C) involves having the AI write the prompt itself, which is unnecessary when the team already has clear examples. By using Few-shot prompting, the tester "conditions" the model's latent space to prioritize the provided format, ensuring that all 500 generated test cases maintain consistency with the HTTP methods, headers, and assertion logic defined in the exemplars.

質問 # 28
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

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

正解：C

質問 # 29
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?

A. Reasoning error in risk calculation logic
B. No defect; this is acceptable
C. Hallucination
D. Dataset bias toward UI features

正解：A

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 # 30
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CT-GenAI有用なテストガイド資料は、最も重要な情報を最も簡単な方法でクライアントに提示するので、CT-GenAI有用なテストガイドを学習するための時間とエネルギーはほとんど必要ありません。クライアントは、テストの学習と準備に20〜30時間しかかかりません。仕事や学習などで忙しい人にとっては、これは良いニュースです。なぜなら、テストの準備に十分な時間がないことを心配する必要がなく、主なことをゆっくりとできるからです。 CT-GenAI学習実践ガイドをご覧ください。ですから、CT-GenAI試験の教材の大きな利点であり、クライアントにとって非常に便利です。

CT-GenAI的中問題集: https://www.certshiken.com/CT-GenAI-shiken.html

P.S. CertShiken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=1-Bn8hAVf6h6QlZQTpSxkrfxUDYbkrsH2