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CCDV-F 試験練習問題

Claude Certified Developer – Foundations

CCDV-F の PDF 練習問題。資料の言語は 英語です。購入はこの試験のみで、自動更新はありません。

試験概要

CCDV-F は「Claude Certified Developer – Foundations」の認定試験です。試験形式、時間、対応言語は以下をご確認ください。最新の出題範囲や受験条件は公式サイトをご参照ください。

Claude Partner Network 会員向けの試験として案内されています。受験資格、対応言語、日程は Partner Academy でご確認ください。

試験コード
CCDV-F
公式の試験・資格名
Claude Certified Developer – Foundations
試験形式
Proctored, identity-verified certification assessment delivered through Pearson
受験可能な言語
公開されている公式資料から全対応言語を確認できませんでした。公式の予約ページをご確認ください。

公式試験の対応言語と練習問題ファイルの言語は異なります。試験の版と言語は公式の予約ページでご確認ください。

公式情報： 公式サイト
情報確認日：2026-10-20

対応言語

  • 英語
  • 簡体字中国語
購入前にお試し

最初の 20 問を無料プレビュー

全 53 問

題庫の元の順序で表示します。回答欄を開くと、正解と収録されている解説を確認できます。

問題の言語：英語。問題文・選択肢・正解・解説は元の題庫の言語で表示します。

免責事項：ExamPico の問題はすべてインターネット上の情報を整理したもので、学習参考用として提供しています。

問題 1

Question#1Topic1

You are starting a new Claude application and have a small set of well-labeled examples that demonstrate the desired output format. You want to use these examples to guide Claude’s behavior. How would you guide the application’s behavior?

  1. A. Embed the examples in a database and retrieve them at runtime as reference material for the team.
  2. B. Use zero-shot prompting and rely on the model’s general capability to produce the desired output format consistently across all incoming requests.
  3. C. Train a custom model on the labeled examples before deployment so that the application does not need to include the examples in any prompt during operation.
  4. D. Use multi-shot prompting by including the labeled examples in the prompt so Claude can match the desired output format on each request.
正解と解説を表示

正解

D

解説

Multi-shot (few-shot) prompting includes labeled examples directly in the prompt so Claude can match the desired output format. Source: https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/multishot-prompting

問題 2

Question#2Topic1

Your Claude application’s content policy specifies categories of content it should not produce under any circumstance. The application currently has no mechanism to enforce this policy, and content matching these categories is appearing in the application’s output. How would you enforce the content policy?

  1. A. Enhance the system prompt to contain explicit instructions for the categories to avoid, complete with examples of each category. Treat the strengthened prompt as the primary enforcement mechanism for the application’s content policy across all responses.
  2. B. Remove the content policy entirely and let any output reach users during normal operation, accepting whatever content the application produces in response to incoming traffic.
  3. C. Add deterministic output filtering that checks responses against the content policy before they reach users.
  4. D. Move enforcement to users by asking them to report content policy violations after the violating content has already reached them in the application’s responses.
正解と解説を表示

正解

C

解説

Content policy enforcement requires deterministic output filtering before responses reach users; prompt instructions alone are insufficient as the sole enforcement layer. Source: https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/handle-streaming-refusals

問題 3

Question#3Topic1

A new Claude model release includes performance improvements for several reasoning tasks but has changed the format of its responses to system prompts that use multi-section instructions. Your application uses multi-section system prompts heavily. Initial evaluation on the application’s actual workload shows the new model performs 8 percent better on reasoning tasks but produces malformed output on roughly 3 percent of requests because of the format change. The team is debating whether to upgrade.
How would you decide?

  1. A. Upgrade and add a downstream validation step that catches the 3 percent malformed output before it reaches users, treating the validation step as the team’s mitigation for the format change.
  2. B. Adapt the application’s system prompt to the new model’s format expectations and re-evaluate, then upgrade only if the adapted prompt eliminates the malformed output while preserving the reasoning improvements.
  3. C. Stay on the previous model permanently to avoid the malformed output rate and any future format changes that subsequent model releases might introduce in the application.
  4. D. Upgrade immediately, because the 8 percent reasoning improvement outweighs the 3 percent malformed output rate across the application’s typical request distribution.
正解と解説を表示

正解

B

解説

When a new model changes response format, adapt the system prompt to the new format expectations and re-evaluate before upgrading, rather than accepting malformed output or staying on the old model permanently. Source: https://docs.anthropic.com/en/docs/about-claude/models/migrating-to-claude-4

問題 4

Question#4Topic1

Your team’s Claude application has been in production for a year, and the team has decided to formalize its testing strategy. Currently, the team writes ad- hoc tests for individual features but has no overall testing approach. What testing approach would you formalize?

  1. A. Adopt a test-driven development practice where unit tests are written before each feature is implemented and must pass before code is merged.
  2. B. Define unit tests for individual functions, integration tests for the Claude integration, and end-to-end tests for critical user flows, applied consistently across the codebase.
  3. C. Continue writing ad-hoc tests as features ship and introduce a peer review step to ensure each test adequately covers the feature being released.
  4. D. Define a single testing approach that uses end-to-end tests and apply it consistently across all new features as they are added to the codebase.
正解と解説を表示

正解

B

解説

A formal testing strategy for Claude applications should include unit tests for individual functions, integration tests for the Claude integration, and end-to-end tests for critical user flows. Source: https://docs.anthropic.com/en/docs/test-and-evaluate/develop-tests

問題 5

Question#5Topic1

Your Claude application makes high-volume API calls during business hours and very few calls overnight. The team is concerned about staying within rate limits during peak hours and wants to understand how the Claude API enforces those limits. How would you proceed?

  1. A. Examine the peak-hour request patterns in your application logs and smooth traffic by distributing requests more evenly across the business-hours window.
  2. B. Assess the average payload size of current API calls and consolidate requests where possible to reduce the total number of calls made during peak hours.
  3. C. Review the API documentation for streaming endpoints and evaluate whether migrating peak-hour calls to streaming reduces exposure to rate limit enforcement.
  4. D. Identify the rate limits, design the application to stay within them during peak hours, and use exponential backoff when limits are reached.
正解と解説を表示

正解

D

解説

To stay within Claude API rate limits, identify the applicable limits from documentation, design the application to stay within them during peak hours, and implement exponential backoff when limits are reached. Source: https://docs.anthropic.com/en/api/rate-limits

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