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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agents and Workflows	14.7%	- Claude Agent SDK and Agent Loops 1. Implement and manage custom agent loops 2. Build and configure agents using the Claude Agent SDK - Subagents and Agentic Frameworks 1. Use subagents and coordinate multi-agent workflows 2. Apply appropriate agentic frameworks and orchestration patterns - Agent Architecture and Tradeoffs 1. Select appropriate agent architectures and patterns 2. Evaluate tradeoffs between agentic approaches and traditional workflows
Topic 2: Claude Code	3.1%	- Claude Code Configuration and Usage 1. Use CLAUDE.md and project configuration 2. Configure settings and development environments 3. Use Skills, plugins, and Claude Code capabilities effectively
Topic 3: Prompt and Context Engineering	11%	- Context Engineering 1. Apply context management strategies for agents and long-running workflows 2. Manage context windows and application context - Prompt Engineering 1. Design prompts appropriate to application requirements 2. Apply prompting techniques to improve reliability and output quality
Topic 4: Security and Safety	8.1%	- Safety and Guardrails 1. Use hooks and other mechanisms to enforce application controls 2. Implement safety controls and guardrails - Secure Application Design 1. Protect sensitive data and manage access appropriately 2. Apply secure-by-design practices to Claude-powered applications
Topic 5: Model Selection and Optimization	16.8%	- Performance and Cost Optimization 1. Apply prompt caching and other cost optimization techniques 2. Use batching and other approaches to improve efficiency - Model Selection 1. Select appropriate Claude models for task requirements 2. Evaluate quality, latency, capability, and cost tradeoffs
Topic 6: Tools and MCPs	10.6%	- Model Context Protocol 1. Apply MCP concepts and patterns for connecting models to external capabilities 2. Build and integrate MCP servers - Tool Development and Integration 1. Design and implement custom tools for Claude applications and agents 2. Handle tool schemas, invocation, and tool-use results
Topic 7: Applications and Integration	33.1%	- Claude API and Client SDKs 1. Implement streaming and handle API errors 2. Integrate applications with the Claude API and supported client SDKs 3. Construct and process API requests and responses - Application Development and Integration 1. Handle multimodal and structured application inputs and outputs 2. Integrate Claude capabilities into existing software systems and workflows 3. Build and ship production-grade Claude-powered applications
Topic 8: Eval, Testing, and Debugging	2.6%	- Testing and Debugging 1. Test Claude integrations and agentic systems 2. Diagnose and debug application, agent, and integration issues - Evaluation 1. Interpret evaluation results and improve application quality 2. Design and run evaluations for Claude-powered applications

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q93-Q98):

NEW QUESTION # 93
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?

A. Examine the peak-hour request patterns in your application logs and smooth traffic by distributing requests more evenly across the business-hours window.
B. Identify the rate limits, design the application to stay within them during peak hours, and use exponential backoff when limits are reached.
C. Review the API documentation for streaming endpoints and evaluate whether migrating peak-hour calls to streaming reduces exposure to rate limit enforcement.
D. 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.

Answer: B

Explanation:
The supplied question marks C , and Anthropic's API documentation directly supports it. Claude API rate limits are enforced using dimensions such as requests per minute (RPM), input tokens per minute (ITPM), and output tokens per minute (OTPM) . Anthropic also notes that short traffic bursts can exceed effective limits even when a longer-term average appears acceptable.
Applications should therefore determine the organization's actual configured limits, model peak traffic against those boundaries, throttle or queue work as necessary, and handle 429 responses correctly. Rate-limit responses include a retry-after value indicating when another request should be attempted. Anthropic's official SDKs automatically retry transient connection failures, rate-limit errors, and server errors with exponential backoff by default.
Streaming does not exempt requests from rate limits, making A technically incorrect. Consolidating payloads in B may reduce request count but could increase token consumption and does not by itself address all rate- limit dimensions. D may smooth traffic, but it is only one optimization and does not substitute for rate-limit- aware application logic.
Relevant Claude Developer topics: Claude API Mechanics, rate limits, RPM, ITPM, OTPM, HTTP 429, retry-after, exponential backoff, throttling, and capacity planning .

NEW QUESTION # 94
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 alongside the project's source code and settings.json files in a separate repository from the project's source code.
B. 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.
C. 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.
D. 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.

Answer: C

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 # 95
Your Claude application has multi-step workflows where each step's output is needed only briefly before the agent moves on. The cumulative tool output is filling the context window with content that is no longer relevant.
How would you handle the accumulating tool output?

A. Switch to a smaller Claude model that processes context more efficiently and treat any quality loss as a tradeoff for the cost reduction.
B. Apply tool output pruning to remove tool outputs that are no longer needed by later steps in the workflow.
C. Apply prompt caching to the accumulated tool outputs so the application does not re-pay for the older content on each subsequent step.
D. Keep every tool output in the context indefinitely so the agent has the full record of every step it has executed during the workflow.

Answer: B

Explanation:
Option A applies the correct context-engineering strategy: remove stale tool results once they no longer contribute useful information to subsequent reasoning. Agentic workflows frequently accumulate search results, file contents, API responses, and intermediate artifacts. Keeping all of them indefinitely consumes the finite context window, raises token cost, and can reduce model focus by introducing low-value information.
Anthropic specifically documents tool result clearing for this situation. Context Editing can remove older tool results when the conversation grows, while preserving recent interactions and optionally retaining tools whose results must remain available. Anthropic describes old tool outputs such as retrieved files or search results as candidates for clearing after Claude has processed them.
Prompt caching in B solves a different problem: it can lower cost and latency for repeated static prompt prefixes, but cached tokens still constitute context and therefore do not eliminate context-window pressure. C changes model capability without solving the architectural cause. D maximizes context pollution.
The correct architecture is therefore to preserve high-signal state while pruning ephemeral intermediate outputs. This aligns with Claude Developer coverage of context engineering, long-running agents, context- window management, tool-result clearing, and efficient agent state management. Anthropic's broader context- engineering guidance likewise emphasizes curating the smallest high-signal context necessary for successful inference.

NEW QUESTION # 96
You are implementing a custom tool for your Claude agent. The tool needs to interact with an external pricing service that returns product data.
Which of the following best practices would you apply as you develop this tool?

A. Omit the tool description and let the model infer when to use the tool based on the tool's name and the rest of the prompt context.
B. Define the tool with a loose schema and let the model interpret the inputs flexibly on each call the agent makes.
C. Define the tool with a clear schema, write a precise description for when to call it, and handle pricing service errors explicitly.
D. Implement the tool with no error handling and let the agent loop catch failures whenever the pricing service returns an error during operation.

Answer: C

Explanation:
Option D combines the three key properties of a reliable Claude tool: an explicit contract, clear tool-selection guidance, and controlled execution failure handling. Anthropic's tool documentation defines user tools using a name, detailed description, and JSON input_schema. The description should explain what the tool does, when it should and should not be used, parameter semantics, and relevant limitations. Anthropic emphasizes that precise descriptions materially improve Claude's ability to select the correct tool.
A clear schema prevents ambiguous parameter interpretation and allows validation before calling the external pricing API. Where stronger guarantees are required, Anthropic also supports strict tool use, which constrains generated tool inputs to the declared JSON Schema.
The application's execution layer must also convert pricing-service failures into explicit, handled error paths rather than uncontrolled exceptions. A deprives Claude of critical selection information. B increases malformed-call risk. C delegates infrastructure reliability to the reasoning loop instead of implementing appropriate integration error handling.
Therefore, D represents production-quality custom-tool construction. Relevant Study Guide topics: custom tools, JSON Schema, tool descriptions, validation, external API integration, and error handling.

NEW QUESTION # 97
Your team is integrating Claude into an existing REST API service. The service handles concurrent requests, and you are deciding how to structure the Claude API calls within the existing async codebase.
How would you structure the Claude calls?

A. Use the Claude SDK's async client and structure Claude calls as awaitable operations alongside the rest of the service's async code.
B. Run Claude API calls in a separate thread pool isolated from the rest of the service so the main event loop is not affected.
C. Make synchronous Claude API calls and block the async event loop until each call completes before processing the next request.
D. Replace the existing async REST service with a synchronous service to match the call style of synchronous Claude SDK clients.

Answer: A

Explanation:
Option C is the natural integration model for an application that already uses asynchronous I/O. Claude API calls are network-bound operations, so blocking the application's event loop while waiting for responses would reduce concurrency and impair throughput. The Anthropic Python SDK explicitly provides AsyncAnthropic, and its documented usage awaits client.messages.create() directly. It also supports an aiohttp backend when improved asynchronous concurrency is desired.
This means Claude requests can participate in the same cooperative asynchronous execution model as other database, HTTP, or service calls. While one request waits for remote I/O, the runtime can continue serving other work rather than dedicating the event loop to an idle blocking operation.
A can be used when integrating unavoidable blocking libraries into async software, but it adds thread-pool management when an official asynchronous client already exists. B needlessly redesigns a concurrent REST service around a synchronous integration. D is specifically harmful because blocking the event loop prevents normal concurrent request processing.
Therefore, C uses the abstraction provided for exactly this architecture. Relevant Study Guide topics: Claude SDK clients, asynchronous APIs, awaitable I/O, concurrency, REST-service integration, and scalable application architecture.

NEW QUESTION # 98
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