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Anthropic CCDV-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q23-Q28):

NEW QUESTION # 23
Your Claude agent has access to a tool that retrieves customer records. A teammate has noticed that the agent occasionally calls the tool with arguments the schema does not declare, and the tool's downstream service returns an error each time. The teammate proposes loosening the schema so the tool accepts whatever arguments the model produces.
How would you respond?

A. Add a system prompt instruction telling the model to produce schema-conforming arguments, treating the prompt instruction as the primary mechanism for keeping the agent's tool calls valid.
B. Loosen the schema as the teammate proposed so the downstream service receives every call the agent makes during normal operation.
C. Keep the schema strict, validate arguments before dispatching, and return a structured error so the agent can retry.
D. Remove the schema entirely and rely on the downstream service to reject invalid calls, treating the downstream service as the team's primary enforcement layer.

Answer: C

Explanation:
The supplied question identifies B as the intended answer. Tool schemas are contracts between Claude and executable application code. When the downstream service accepts only a defined set of arguments, relaxing that schema merely shifts invalid data farther into the system and increases runtime failures.
Anthropic's current tooling provides an even stronger implementation of this principle through strict tool use
. Setting strict: true constrains tool inputs to the declared JSON Schema, preventing undeclared properties, missing required values, and incompatible parameter types where the supported schema subset is used.
Anthropic explicitly recommends strict tool use for validated parameters, type-safe function calls, and reliable agentic workflows.
In a non-strict or legacy implementation, the application should still validate arguments before dispatch and convert invalid calls into structured tool errors that Claude can interpret and potentially correct. A prompt instruction can reinforce behavior, but it should not replace deterministic validation. C and D weaken the system boundary and knowingly send invalid calls downstream.
Therefore, maintain the contract rather than adapting the contract to malformed model output.
Relevant Claude Developer topics: Agent Construction, tool schemas, strict tool use, JSON Schema, parameter validation, structured errors, retry behavior, and defensive execution boundaries .

NEW QUESTION # 24
A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?

A. Switch to a deterministic workflow that handles billing, shipping, and product issues in a fixed sequence.
B. Add detailed prompting that instructs the agent to track which sub-issues have been resolved and which remain.
C. Introduce an orchestrator agent that delegates billing, shipping, and product sub-issues to dedicated subagents.
D. Increase the size of the agent's context window so it can hold the full ticket history at once.

Answer: C

Explanation:
Option C applies an orchestrator-worker architecture to a request containing several distinct domains. Rather than making one agent continuously switch between billing, shipping, and product reasoning, an orchestrator can decompose the ticket, delegate each concern to an appropriately scoped specialist, track completion, and consolidate the resulting recommendations.
Anthropic describes this architecture directly: an orchestrator dynamically breaks down a task, delegates subtasks to worker agents, and synthesizes their results. Anthropic's multi-agent Research system similarly uses a lead agent that coordinates specialized subagents operating with independent contexts.
A rigid workflow is inappropriate because not every ticket contains the same combination or ordering of issues. B can improve behavior but leaves one agent responsible for managing all competing concerns and state. D increases raw context capacity without addressing decomposition or responsibility boundaries.
C is therefore the strongest architectural change when separate issue categories can be handled independently and then reconciled by a coordinating component. Relevant Study Guide topics: orchestrator-workers, subagents, delegation, task decomposition, context isolation, coordination, and synthesis.

NEW QUESTION # 25
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. Identify the rate limits, design the application to stay within them during peak hours, and use exponential backoff when limits are reached.
B. Examine the peak-hour request patterns in your application logs and smooth traffic by distributing requests more evenly across the business-hours window.
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: A

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 # 26
You are designing a Claude application that will require structured JSON output for downstream processing.
The output schema is well-defined, and downstream systems will reject malformed JSON.

A. Define a clear schema, structure the prompt to request output in that schema, and validate Claude's output against the schema before passing it downstream.
B. Avoid structured output and use free-form text everywhere instead, on the grounds that free-form text is more flexible and handles edge cases better than structured schemas.
C. Define a clear schema and structure the prompt to request output in that schema, with downstream systems handling any validation needed.
D. Structure the prompt to request output in a schema that is described in plain English, with downstream systems parsing whatever shape Claude produces.

Answer: A

Explanation:
Option C establishes the strongest application boundary between probabilistic model generation and deterministic downstream processing. When another component requires JSON with a known contract, the application should explicitly define the expected structure and ensure that model output conforms to it before downstream execution. Anthropic's current Structured Outputs guidance states that structured outputs constrain responses to a specific schema and are intended to provide valid, parseable data for downstream processing. The current Claude API supports JSON Schema through output_config.format, while SDK helpers can additionally parse and validate returned data.
The underlying engineering principle remains the same even when structured-output enforcement is unavailable: never allow unvalidated model-generated structures to become trusted machine input. Option A provides insufficient contractual control. Option B defines the schema but pushes validation too late, increasing the probability that malformed or semantically invalid data reaches dependent components. Option D sacrifices machine reliability entirely.
Therefore, C correctly combines schema specification, explicit format guidance, and validation. This corresponds to Claude Developer topics covering structured outputs, defensive application design, schema validation, and reliable model-to-system interfaces. The question and options are reproduced from the supplied examination set.

NEW QUESTION # 27
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 alongside the project's source code, with changes reviewed through standard pull request workflows the team applies.
B. Version-control CLAUDE.md alongside the project's source code and settings.json files in a separate repository from the project's source code.
C. 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.
D. 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.

Answer: A

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 # 28
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