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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt and Context Engineering	11%	- Context Engineering - Prompt Engineering - Context Management and Long-Context Techniques
Topic 2: Model Selection and Optimization	16.8%	- Performance Optimization - Model Selection - Model Capabilities and Trade-offs - Cost and Latency Optimization
Topic 3: Agents and Workflows	14.7%	- Agent Patterns and Frameworks - Agent Construction with Claude - Agent Architecture
Topic 4: Tools and MCPs	10.6%	- Building Custom Tools and MCP Servers - Model Context Protocol - Tool Use and Tool Schemas
Topic 5: Applications and Integration	33.1%	- Claude API and Client SDKs - Message Batches and Prompt Caching - Multimodal and Structured Outputs - API Integration and Application Development - Software Engineering Fundamentals - Streaming, Error Handling and Reliability
Topic 6: Claude Code	3.1%	- Claude Code Configuration and Extensibility
Topic 7: Eval, Testing, and Debugging	2.6%	- Evaluation, Testing, and Debugging
Topic 8: Security and Safety	8.1%	- Safety and Responsible Development - Application Security - Secure Tool Use and Guardrails - Prompt Injection and Untrusted Content

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q12-Q17):

NEW QUESTION # 12
You are setting up the configuration management approach for a new Claude Code project. Your team will use CLAUDE.md files and settings.json files to control behavior, and you want to make sure changes are tracked and reviewable.
The configuration management approach would...

A. Version-control CLAUDE.md and settings.json files in a separate repository from the project's source code, so configuration evolves independently from the application code over time.
B. Duplicate CLAUDE.md and settings.json files in multiple repositories to provide redundancy, on the grounds that a single source of truth is risky for project configuration.
C. Version-control CLAUDE.md alongside the project's source code and settings.json files in a separate repository from the project's source code.
D. Version-control CLAUDE.md and settings.json files alongside the project's source code, with changes reviewed through standard pull request workflows the team applies.

Answer: D

Explanation:
D applies configuration as code and is the answer explicitly selected in the supplied examination source.
Claude Code treats project configuration as part of the repository's working context. Anthropic documents project-level CLAUDE.md as the mechanism for providing project instructions and context, while shared project configuration belongs in .claude/settings.json. Crucially, Anthropic states that .claude/settings.json is intended to be checked into source control and shared with the team .
Keeping these files alongside the code establishes a single versioned relationship between application behavior and the configuration that influences Claude Code. Standard pull requests then provide diffs, peer review, ownership controls, CI validation, audit history, and straightforward rollback. A particular commit can therefore reproduce both source code and its associated Claude project configuration.
A creates competing copies and synchronization risk. B artificially separates behavior configuration from the code version it affects. C creates the same problem specifically for settings.json.
Developer-local values remain distinct: .claude/settings.local.json exists specifically for settings that should not be committed.
Relevant Claude Developer topics: Confia Management, CLAUDE.md, settings.json, configuration as code, source control, pull-request review, reproducibility, auditability, and team-shared Claude Code configuration .

NEW QUESTION # 13
A teammate has submitted a pull request that adds a Claude-powered feature to your service. The code works, but the prompt and model selection are hard-coded inline, error handling is missing, and there are no tests for the integration.
What would you request during code review?

A. Request changes that move prompt and model configuration to a configurable location and add tests, treating the missing error handling as a follow-up release item.
B. Approve the pull request as-is, on the grounds that the feature works in the happy path and the missing pieces can be added in follow-up commits.
C. Approve the pull request and add the missing pieces yourself in a follow-up commit so the teammate can move on to other work immediately.
D. Request changes that move prompt and model configuration to a configurable location, add error handling for Claude API failures, and add tests for the integration.

Answer: D

Explanation:
D is the only option that addresses all three identified production-readiness defects. The supplied Claude Developer item explicitly selects D. A functioning happy path is insufficient for a maintainable Claude integration.
Prompt and model selection are configuration concerns that will change as prompts are evaluated, model versions evolve, or environments require different behavior. They should therefore be separated from unrelated business logic rather than scattered as inline constants. Claude API failures must also be handled deliberately. Anthropic documents typed SDK exceptions and defined HTTP error categories, including invalid requests, authentication failures, rate limits, server errors, overload, and timeouts. The official SDKs additionally retry appropriate transient failures.
Tests are required to verify the integration boundary, including successful behavior, malformed or unexpected responses, API error handling, and critical user workflows. Approving code without those controls pushes known reliability debt directly into production.
A and B knowingly merge incomplete production behavior. C improves configuration and test coverage but leaves a known API-failure path unhandled.
Relevant Claude Developer topics: SW Eng Foundations, code review, separation of configuration, error handling, integration testing, API resilience, maintainability, and production readiness .

NEW QUESTION # 14
A teammate has asked how to extend Claude Code with a custom Skill that the team can invoke during sessions. The Skill consists of a set of instructions and a few support scripts the team wants Claude to be able to call when the Skill is loaded.
Where is the right place to define the Skill?

A. Define the Skill in a personal scratch directory on each developer's machine and load it manually before each Claude Code session that needs it.
B. Define the Skill as a long inline instruction at the top of every CLAUDE.md file in the team's repositories so Claude has access to it on every session.
C. Define the Skill in a Skills directory recognized by Claude Code, where Claude can discover and load it during sessions for the team's repositories.
D. Define the Skill inside the application's source code as a regular library module and call it from the application code instead of from Claude Code.

Answer: C

Explanation:
Option C matches Claude Code's documented Skill architecture. Agent Skills are filesystem-based extension artifacts rather than ordinary application modules or repeated prompt fragments. A Skill is represented by a directory containing a required SKILL.md file and can include optional supporting scripts, templates, examples, and reference material.
Anthropic documents project Skills under .claude/skills/ < skill-name > /SKILL.md. Project-level Skills can be shared through Git and automatically discovered when Claude Code loads project settings. Supporting scripts can reside alongside the Skill and be referenced from SKILL.md.
A incorrectly embeds reusable procedural material into every CLAUDE.md file, creating duplication and loading instructions even when they are irrelevant. B creates a conventional source-code library but does not register a Claude Code Skill. D makes the capability dependent on undocumented, developer-specific setup and undermines team reuse.
Therefore, C uses the extension mechanism specifically designed for discoverable, reusable Claude capabilities. Relevant Study Guide topics: Agent Skills, .claude/skills, SKILL.md, supporting resources, filesystem discovery, project-level configuration, and reusable Claude Code capabilities.

NEW QUESTION # 15
You are building a Claude application that processes 10,000 customer emails overnight to extract structured data. The work is non-interactive, runs once daily, and has a flexible completion window of several hours.
Which Claude API would you use?

A. The real-time Messages API, processing the emails one at a time sequentially to ensure consistent ordering of results.
B. The Batch API, which is designed for non-interactive workloads with flexible completion windows.
C. The streaming responses API to process each email and return partial results to a database as the model generates them.
D. The real-time Messages API with concurrent requests to process the emails as fast as possible during the overnight window.

Answer: B

Explanation:
Option A is correct because the Message Batches API is designed for asynchronous, high-volume processing where results do not need to be returned interactively. Anthropic's API reference states that a Message Batch can contain many independent Messages requests and may take up to 24 hours to complete. That makes it appropriate for 10,000 overnight email-extraction jobs with a several-hour completion window.
Streaming in B solves a different requirement: it exposes partial response events while a single request is being generated, which is valuable for interactive user experiences or long-running synchronous requests, but it does not provide the workload-management advantages of a batch job. C processes items sequentially and unnecessarily sacrifices throughput. D can increase throughput with concurrent real-time calls, but it adds concurrency management and rate-limit pressure when the workload explicitly tolerates asynchronous completion.
The batch design also lets each request carry a custom identifier so results can be matched back to source emails even if completion order differs. Therefore, A is the intended Claude API choice. Relevant Study Guide topics: Message Batches API, asynchronous processing, high-volume workloads, request correlation, throughput, and non-interactive application design.

NEW QUESTION # 16
A Claude application that worked well in testing is now occasionally returning outputs that mention information not present in the input. The development team initially assumed the model was hallucinating, so they asked you to troubleshoot.
What would you do first?

A. Examine production traces to identify whether the issue is hallucination by the model, context loss, prompt injection, or another failure mode before recommending a fix.
B. Add a system prompt instruction telling the model not to invent information, on the grounds that prompt-level instructions are the fastest fix for hallucination concerns.
C. Replace the current model with a larger one to reduce the chance of hallucination, on the grounds that larger models tend to hallucinate less in typical applications.
D. Apply a retrieval-augmented generation pattern to ground the responses in source content before any further investigation of the production traces.

Answer: A

Explanation:
Option A follows disciplined production debugging: diagnose the actual failure mode before changing architecture or prompts. An output containing unsupported information might indeed be hallucination, but similar symptoms can result from stale conversation state, incorrect retrieval, unexpected tool output, prompt injection, incorrect request construction, or mismatched model/configuration versions.
A production trace should capture the user input, system instructions, relevant conversation history, retrieved content, tool calls and results, model/version, request parameters, response, and identifiers necessary to compare successful and failing cases. This establishes whether the model invented a fact or whether that fact entered context through another path.
B changes the model before establishing causality. C may eventually be useful if the confirmed problem is insufficient grounding, but implementing RAG before diagnosis can hide rather than explain the defect. D similarly changes prompting before verifying that prompt behavior is responsible.
The engineering sequence should be observe, reproduce, classify the failure, form a hypothesis, apply a targeted correction, and validate the correction with evaluations. Relevant Study Guide topics: production troubleshooting, observability, tracing, hallucination analysis, prompt injection, context failures, regression diagnosis, and lifecycle monitoring.

NEW QUESTION # 17
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