시험에 불합격하면 응시료를 다시 지불해야 하므로 한 번의 합격이 무엇보다 중요합니다. Fast2test의 CCDV-F 모의고사 97문항으로 실전 감각을 미리 익혀 재응시 비용 부담을 줄여 보시기 바랍니다. 2026년에도 꾸준히 업데이트되는 자료입니다.

Anthropic CCDV-F 시험 개요:

Anthropic CCDV-F 시험 요강 주제:

Anthropic CCDV-F 관련 궁금증 해결 가이드

최신 Claude Certified Developer CCDV-F 무료샘플문제

문제 #1

Your team is choosing how to add a capability to a Claude application. You want to apply the appropriate option, whether built-in tool, custom tool, Skill, or MCP server, based on the use case.
You would choose the option that...

A. Matches the use case's scope, reuse needs, and integration context.
B. Is the most familiar to the team based on prior experience with similar capabilities.
C. Is the newest available option among built-in tools, custom tools, Skills, and MCP servers.
D. Is the easiest to build given the team's current development tools and workflows.

문제 #2

You are configuring Claude Code for a new project. The team needs to set permissions, default model selections, and environment-specific behavior at the project level so the configuration is consistent across all developers working on the repository.
The Claude Code mechanism you would use is...

A. A shared spreadsheet that lists configuration values for team members to reference and update by hand as the project evolves.
B. The settings.json file, scoped at the project level so the configuration applies consistently across developers and persists with the repository.
C. Environment variables that each developer sets on their own machine when working with Claude Code on the project.
D. A system prompt embedded in every Claude Code conversation by each developer at the start of every session in the project.

문제 #3

A teammate has asked you to explain the difference between context engineering and prompt engineering.
They have heard the terms used interchangeably and are unsure how each applies to a Claude application that processes long-running multi-step tasks.
How would you describe the distinction?

A. Prompt engineering shapes individual prompts for specific outputs, while context engineering manages how content flows across turns and steps and takes steps to keep relevant state visible.
B. Prompt engineering focuses on the model's response, while context engineering focuses on the user's input across many sessions in a long-running multi-step Claude application.
C. Prompt engineering is the older term for prompt design, while context engineering is the newer term that has replaced it in modern Claude applications across the industry.
D. Prompt engineering and context engineering each address content the team gives Claude, but the team can group them under a single workflow because the practices use overlapping techniques.

문제 #4

You are setting up a Claude application that requires API keys for several external services.
What is the best way to store the keys?

A. Use a single shared key across all external services, so any developer working on the application can find the keys easily during development.
B. Store the keys in a secrets manager or environment-specific configuration that is not checked into source code, and load them at runtime.
C. Email the keys to each developer as needed and have each developer paste the keys into their local environment when they begin working on the application's code.
D. Put the keys in the application's configuration file and check the configuration file into the team's repository alongside the rest of the source code.

문제 #5

Your team is preparing a new Claude application for production, and the product team has asked for a cost projection. The team needs to estimate the cost based on expected request volume, average input length, and average output length. How would you build the projection?

A. Build a cost model based on expected request volume and the chosen model's pricing, treating average input and output token counts as variables to be estimated post-launch.
B. Build a cost model that combines expected request volume and average input tokens, treating output tokens as a small enough share of cost to leave out of the projection.
C. Build a cost model that combines expected request volume, average input tokens, average output tokens, the chosen model's pricing, and any caching benefits.
D. Build a cost model that uses the average per-request cost from a similar Claude application the team built last year, scaling that figure by expected request volume.

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