Exam Code: CCDV-F
Exam Name: Claude Certified Developer - Foundations
Price: $68.00 $58.88
Exam Questions: 678 Q&As
Last Updated: 2026-08-27
Prepare for the CCDV-F exam with focused practice questions and clear explanations designed around building, integrating, and shipping production-grade solutions with Anthropic's Claude platform.
CCDV-F is the exam for the Claude Certified Developer - Foundations certification. It validates foundational skills in API integration, agents and workflows, tools and MCPs, prompt and context engineering, evaluation, security, model selection, and Claude Code.
The CCDV-F certification is intended for technical professionals who translate requirements into production-ready applications, agents, and workflows using Claude and other LLM-based systems. The audience includes AI and machine-learning engineers, technical leads, and senior software engineers.
The exam contains 53 multiple-choice and multiple-response items, and each item states how many responses to select. Delivery is online proctored and/or at a test center according to program policy. The credential is valid for 12 months, and result reporting includes pass/fail, a scaled score from 100–1,000, and percent-correct by domain.
Candidates should have one to five years of software engineering experience and at least six months of hands-on work with Claude or comparable LLM-based systems. Proficiency in Python and/or TypeScript is recommended.
You should also be fluent with REST APIs and CLI tools and have a working understanding of LLM fundamentals, agents, context management, and MCP.
Review questions organized around the eight CCDV-F domains so you can identify gaps and prioritize your preparation.
Use detailed explanations to understand agent architecture, Claude Code configuration, prompt workflows, provenance, and production integration decisions.
Study from a downloadable PDF or use interactive exam-style software with practice and exam modes, progress tracking, and instant feedback.
Eligible purchases include three months of content updates and access to customer support when you need help with the study product.
The following questions are educational practice material and are not official Anthropic exam questions.
Scenario: Developer Productivity with Claude You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses built-in tools (Read, Write, Bash, Grep, Glob) and integrates with MCP servers. An engineer asks the agent to explain how the "user export" capability works in a legacy repository. The final answer confidently covers REST controllers and serializers, but misses scheduled exports, admin-triggered jobs, and CLI invocations. Logs show every subagent completed successfully; their prompts were "inspect export controller," "trace export API request," and "summarize export endpoint tests.". What should you change first to improve coverage on similar broad codebase questions?
Correct answer: D
Explanation: The root issue is the coordinator's task decomposition, not subagent execution. The subagents completed successfully, but all assigned prompts focused on the REST API path, so the final answer missed other relevant workflows such as scheduled jobs, admin actions, and CLI commands. For broad codebase exploration, the coordinator should first reason about the possible surfaces where a capability may appear, such as controllers, jobs, commands, tests, database models, and integrations. It can then delegate distinct areas to specialized subagents and aggregate their findings into a complete explanation. Asking synthesis to infer missing workflows is an anti-pattern because it encourages unsupported guesses. Having every subagent read every matching file creates context bloat and attention dilution, while a fixed pipeline wastes work and ignores the need for dynamic routing based on query complexity. The underlying principle is that a coordinator-subagent architecture depends on the coordinator to preserve coverage and routing discipline. Learn more about agent orchestration patterns in the Agent SDK documentation.
Scenario: Multi-Agent Research System You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports. Your document-analysis subagent receives 45 reports and policy papers in a single pass before synthesis. The final outputs cite many sources but miss source-specific caveats, merge incompatible methodologies, and sometimes contradict earlier extracted facts. What workflow change would most effectively improve reliability?
Correct answer: A
Explanation: Prompt chaining is appropriate when a workflow has predictable stages that benefit from focused attention. In this case, the reliable structure is to analyze each source or small source group first, producing structured findings with caveats and provenance, then run a separate cross-source integration pass before report generation. The architectural principle is to avoid forcing one model call to juggle too many competing responsibilities at once. A focused local pass improves extraction quality, while the integration pass explicitly compares evidence, identifies contradictions, and prepares a coherent synthesis for the report agent. Simply asking the synthesis agent to be more careful leaves the overloaded design unchanged. Random parallel partitioning can improve throughput, but concatenating summaries without an integration step risks fluent but inconsistent reporting. Using a fixed iteration limit or citation-count target is also unreliable because those signals do not measure whether the system handled methodological conflicts or caveats correctly. For more background on breaking complex tasks into focused prompt sequences, see Prompt Engineering and Agent SDK.
Scenario: Developer Productivity with Claude You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses built-in tools (Read, Write, Bash, Grep, Glob) and integrates with MCP servers. A senior engineer reports that Claude Code consistently follows your team's codebase exploration notes, legacy module warnings, and boilerplate conventions. New engineers who clone the repository see generic behavior instead, and no repository files changed when the senior engineer originally added those notes. What is the most effective way to make this guidance consistent for the team?
Correct answer: C
Explanation: Project-scoped configuration is the right fix when Claude Code behavior must be consistent for everyone working in the same repository. Guidance stored in a project-level CLAUDE.md, such as .claude/CLAUDE.md or a root CLAUDE.md, can be committed and distributed through normal version control workflows. The underlying principle is scope alignment: personal preferences belong in user-level memory, while team standards and repository-specific context belong in project-level configuration. If guidance was added locally but no repository files changed, the likely issue is that it was stored in a personal location rather than a shared project file. Copying personal memory files, relying on optional slash commands, or pasting conventions at session start all create process-dependent behavior and configuration drift. These approaches are fragile because they depend on each developer remembering the same manual steps. Learn more about Claude Code memory hierarchy and shared project guidance in CLAUDE.md Configuration and the broader Claude Code Overview.
Scenario: Developer Productivity with Claude You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses built-in tools (Read, Write, Bash, Grep, Glob) and integrates with MCP servers. Your repository's root CLAUDE.md has grown to 1,200 lines covering testing, API conventions, migration rules, deployment checks, and review norms. Engineers complain that small standards changes cause frequent merge conflicts, and Claude sometimes appears distracted by unrelated guidance. The team wants shared, version-controlled instructions, but needs a more maintainable layout without relying on developers to remember extra steps. What should you do?
Correct answer: A
Explanation: Modular Claude Code configuration is the right fit when a shared CLAUDE.md has become too large to maintain effectively. Splitting guidance into topic-specific files under .claude/rules/ keeps standards version-controlled while reducing the operational burden of editing and reviewing one monolithic instruction file. The underlying principle is to keep persistent project context organized by concern. Testing, API conventions, deployment rules, and review norms can evolve independently, making changes easier to review and reducing accidental conflicts between unrelated guidance areas. Moving instructions into ~/.claude/CLAUDE.md breaks team sharing because that file is scoped to one user. Keeping everything in one root file with stronger headings preserves the monolith and relies on attention management rather than configuration structure. Slash commands are the wrong abstraction because they require manual invocation and are better suited to task-specific workflows, not baseline project standards. Learn more about Claude Code memory hierarchy and modular configuration in CLAUDE.md Configuration and related Claude Code concepts in Claude Code Overview.
Scenario: Developer Productivity with Claude You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses built-in tools (Read, Write, Bash, Grep, Glob) and integrates with MCP servers. A coordinator agent delegates codebase exploration to subagents before asking an implementation subagent to generate migration scaffolding. In reviews, engineers find the final proposal often mixes findings from different packages, cites helper functions without file locations, and cannot explain which search result or source file supports a recommended change. The individual subagents found useful facts, but their handoffs were free-form summaries.
What change would best improve downstream reliability while preserving attribution?
Correct answer: A
Explanation: Structured subagent handoffs are the best fit when one agent's findings must be used by another agent for implementation or synthesis. The coordinator should require each exploration subagent to return records that distinguish the discovered fact from metadata such as file_path, symbol, line_range, source_excerpt, and the command or tool result that produced the observation. The underlying principle is that subagents operate with isolated context, so any downstream agent only receives what the coordinator explicitly passes along. Free-form summaries often compress away provenance, which makes later recommendations harder to verify and easier to misattribute across similar packages or duplicated helper functions. Asking a downstream implementation agent to infer locations, adding longer narrative reasoning, or stripping metadata to save space all fail because they treat provenance as optional. In production developer tools, attribution is part of the work product, not decoration, because engineers need to validate recommendations against concrete files and source evidence. Learn more about subagent orchestration in Agent SDK and Claude Code agent patterns in Claude Code Sub-agents.
It validates foundational ability to build, integrate, and ship production-grade applications, agents, and workflows using Anthropic's Claude platform.
It is intended for technical professionals such as AI and machine-learning engineers, technical leads, and senior software engineers who connect business requirements with production implementation.
The exam contains 53 multiple-choice and multiple-response items. Each item states how many responses to select.
The passing score is a scaled score of 720 on a scale of 100–1,000, and the time limit is 120 minutes.
Delivery is online proctored and/or at a test center according to program policy. The stated exam fee is USD $125.
Recommended preparation includes one to five years of software engineering and at least six months with Claude or comparable LLM systems. The credential is valid for 12 months, and reporting includes pass/fail, scaled score, and percent-correct by domain.
No. They are educational practice material. CertQuestionsBank is independently operated and is not affiliated with or endorsed by Anthropic.
Answer each question before viewing the explanation, review the architecture and Claude platform concepts behind missed answers, build authorized practice projects, and verify current behavior with official Anthropic documentation.
Practice across all eight domains, review explanations, and use your results to guide hands-on Claude development preparation.
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