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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Security and Safety	8.1%	- Secure Application Design 1. Apply secure-by-design practices to Claude-powered applications 2. Protect sensitive data and manage access appropriately - Safety and Guardrails 1. Use hooks and other mechanisms to enforce application controls 2. Implement safety controls and guardrails
Applications and Integration	33.1%	- Application Development and Integration 1. Integrate Claude capabilities into existing software systems and workflows 2. Handle multimodal and structured application inputs and outputs 3. Build and ship production-grade Claude-powered applications - Claude API and Client SDKs 1. Integrate applications with the Claude API and supported client SDKs 2. Construct and process API requests and responses 3. Implement streaming and handle API errors
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
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
Claude Code	3.1%	- Claude Code Configuration and Usage 1. Use Skills, plugins, and Claude Code capabilities effectively 2. Configure settings and development environments 3. Use CLAUDE.md and project configuration
Agents and Workflows	14.7%	- Agent Architecture and Tradeoffs 1. Select appropriate agent architectures and patterns 2. Evaluate tradeoffs between agentic approaches and traditional workflows - 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
Tools and MCPs	10.6%	- Tool Development and Integration 1. Handle tool schemas, invocation, and tool-use results 2. Design and implement custom tools for Claude applications and agents - Model Context Protocol 1. Build and integrate MCP servers 2. Apply MCP concepts and patterns for connecting models to external capabilities
Prompt and Context Engineering	11%	- Prompt Engineering 1. Design prompts appropriate to application requirements 2. Apply prompting techniques to improve reliability and output quality - Context Engineering 1. Apply context management strategies for agents and long-running workflows 2. Manage context windows and application context

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q33-Q38):

NEW QUESTION # 33
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. Structure the prompt to request output in a schema that is described in plain English, with downstream systems parsing whatever shape Claude produces.
B. Define a clear schema and structure the prompt to request output in that schema, with downstream systems handling any validation needed.
C. 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.
D. 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.

Answer: D

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 # 34
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 inside the application's source code as a regular library module and call it from the application code instead of from Claude Code.
B. 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.
C. 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.
D. 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.

Answer: B

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 # 35
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, add error handling for Claude API failures, and add tests for the integration.
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 and add tests, treating the missing error handling as a follow-up release item.

Answer: A

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 # 36
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. Apply a retrieval-augmented generation pattern to ground the responses in source content before any further investigation of the production traces.
B. 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.
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. 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.

Answer: B

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 # 37
A team has deployed a multi-agent system in which a primary agent decomposes user requests and delegates subtasks to three specialized subagents: one for data retrieval, one for analysis, and one for report generation.
In production, the team observes that subagents are making redundant tool calls, occasionally exceeding token budgets, and sometimes producing outputs that contradict each other - all of which the primary agent passes along without catching.
What is the most appropriate way to address these failures?

A. Strengthen the primary agent's management layer to enforce per-subagent tool budgets, validate outputs against a defined schema before passing them forward, and establish explicit handoff contracts between stages.
B. Collapse the three subagents into a single large-context model call that handles retrieval, analysis, and generation in one pass.
C. Give each subagent read access to the other subagents' outputs so they can identify and resolve contradictions without routing back through the primary agent.
D. Add retry logic to each subagent so that when a tool call fails, the subagent retries automatically before escalating - and configure each subagent to log its tool calls and outputs to a shared trace so the team can audit redundancy and contradictions after the fact.

Answer: A

Explanation:
C addresses the failures at the correct architectural layer: orchestration and supervision . The supplied exam item identifies C as correct. The primary agent is responsible not merely for forwarding subordinate output but for governing delegation, resource usage, stage boundaries, and result quality.
Anthropic's current guidance recommends explicit control over subagent use because excessive delegation multiplies latency and cost. Its documentation specifically supports deterministic limits on subagent spawning and SDK budget controls such as max_budget_usd. Tool contracts can also use defined input schemas and strict validation so malformed data cannot silently propagate between processing stages.
Explicit handoff contracts are equally important. Retrieval should provide an agreed structure to analysis; analysis should provide validated findings to report generation; and the manager should reject, retry, reconcile, or escalate outputs that violate those contracts.
A improves observability but mainly detects problems after they occur. B destroys context isolation and can create additional coupling. D abandons useful specialization instead of correcting deficient supervision.
Relevant Claude Developer topics: Agent Patterns, orchestrator/subagent architecture, delegation, budgets, handoff contracts, schema validation, output reconciliation, and multi-agent governance .

NEW QUESTION # 38
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