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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Agents and Workflows	14.7%	- Claude Agent SDK usage - Agent architecture principles - Workflow vs autonomous agents - Memory and context management
Evaluation, Testing, and Debugging	2.6%	- Output evaluation and validation - Error handling and debugging
Claude Code	3.1%	- Claude Code configuration and usage
Security and Safety	8.1%	- AI application security - Guardrails and safety controls
Tools and Model Context Protocol (MCP)	10.6%	- Tool integration and usage - MCP server development
Model Selection and Optimization	16.8%	- Latency and performance trade-offs - Cost and token optimization - Claude model family characteristics
Prompt and Context Engineering	11%	- Context window management - Structured output handling - Prompt design and structuring
Applications and Integration	33.1%	- Streaming and Batch API - Vision capabilities - Claude Messages API - SDK and third-party integration

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q25-Q30):

NEW QUESTION # 25
Your Claude application's content policy specifies categories of content it should not produce under any circumstance. The application currently has no mechanism to enforce this policy, and content matching these categories is appearing in the application's output.
How would you enforce the content policy?

A. Move enforcement to users by asking them to report content policy violations after the violating content has already reached them in the application's responses.
B. Add deterministic output filtering that checks responses against the content policy before they reach users.
C. Remove the content policy entirely and let any output reach users during normal operation, accepting whatever content the application produces in response to incoming traffic.
D. Enhance the system prompt to contain explicit instructions for the categories to avoid, complete with examples of each category. Treat the strengthened prompt as the primary enforcement mechanism for the application's content policy across all responses.

Answer: B

Explanation:
Option D is the strongest enforcement design because an unconditional content policy requires an application- level control between model generation and user delivery. Prompt instructions are valuable for steering Claude, but they are probabilistic controls and should not be treated as the sole enforcement mechanism when prohibited categories must never be exposed.
Anthropic's guardrail guidance recommends layered safeguards including screening, validation, monitoring, and filtering rather than relying exclusively on prompts. Its prompt-leak guidance specifically recommends output screening and post-processing, including deterministic techniques such as keyword matching, regular expressions, or other text-processing mechanisms where appropriate.
A improves the probability of policy compliance but cannot guarantee that every generated response will satisfy an externally defined application policy. B explicitly abandons the requirement. C detects violations only after exposure, which is unsuitable when the content must not reach users.
A production architecture can combine system instructions, structured classification, policy engines, deterministic rules, and model-based moderation, but the decisive requirement is enforcement before output delivery. Relevant Claude Developer topics are guardrails, output filtering, content moderation, deterministic enforcement, defense in depth, safe application boundaries, and production Claude application design.
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NEW QUESTION # 26
Your application uses the Messages API to handle multi-turn conversations. Each new turn resends the entire conversation history, and your token costs are growing as conversations get longer. You suspect there is a more efficient approach.
How would you address this?

A. Switch to the Batch API for every turn so the per-call cost is reduced, treating the batch as the team's primary cost-control mechanism for multi-turn work.
B. Use prompt caching to reuse the static portions of the conversation context across turns instead of paying for them at the normal input-token rate on every request the application sends.
C. Summarize each conversation after every turn and replace the full history with the summary on the next request, reducing token count at the cost of fidelity.
D. Truncate every conversation to the last two messages so that token usage stays low and costs remain predictable across the application's normal operation.

Answer: B

Explanation:
Option A is the technically verified answer. Anthropic documents that multi-turn agentic requests resend the growing conversation context on subsequent turns. Prompt caching allows repeated prompt prefixes-such as system instructions, tool definitions, and prior unchanged conversation content-to be reused at the lower cache-read rate rather than repeatedly charged as ordinary uncached input. Anthropic specifically identifies caching repeated context as a major production cost optimization.
B is incorrect even though the supplied PDF contains a duplicated screenshot in which B appears selected.
The Batch API is designed for asynchronous workloads that can tolerate delayed completion; it is not the appropriate mechanism for every interactive turn in a multi-turn conversation. Because the user requested verified answers rather than blindly reproducing marked selections, A is retained.
C discards potentially essential conversation state arbitrarily. D is a valid context-management technique in some long-running workflows, but summarizing after every turn creates additional model work and loses detail; it does not exploit repeated-prefix caching.
Therefore, A directly addresses the stated cost pattern while preserving conversation fidelity. Relevant Study Guide topics: Messages API, stateless conversation history, prompt caching, cached input tokens, multi-turn applications, token economics, and cost optimization.

NEW QUESTION # 27
A new Claude model release includes performance improvements for several reasoning tasks but has changed the format of its responses to system prompts that use multi-section instructions. Your application uses multi- section system prompts heavily. Initial evaluation on the application's actual workload shows the new model performs 8 percent better on reasoning tasks but produces malformed output on roughly 3 percent of requests because of the format change. The team is debating whether to upgrade.
How would you decide?

A. Upgrade immediately, because the 8 percent reasoning improvement outweighs the 3 percent malformed output rate across the application's typical request distribution.
B. Stay on the previous model permanently to avoid the malformed output rate and any future format changes that subsequent model releases might introduce in the application.
C. Upgrade and add a downstream validation step that catches the 3 percent malformed output before it reaches users, treating the validation step as the team's mitigation for the format change.
D. Adapt the application's system prompt to the new model's format expectations and re-evaluate, then upgrade only if the adapted prompt eliminates the malformed output while preserving the reasoning improvements.

Answer: D

Explanation:
The supplied examination page marks B . The scenario already demonstrates why model upgrades must be treated as evaluated software changes rather than automatic replacements: the new model improves one metric while introducing a regression in another.
Anthropic's official model-selection guidance recommends creating benchmark tests specific to the application's use case, testing models with the application's actual prompts and data, comparing response quality and edge-case performance, and weighing performance against operational tradeoffs. Therefore, the correct action is to adapt the multi-section system prompt to the new model's behavior and repeat the evaluation. Only after the formatting regression is eliminated-or reduced below an explicitly acceptable threshold-should the upgrade proceed.
A incorrectly assumes that an 8% reasoning improvement numerically compensates for a 3% malformed- output rate; these metrics measure different consequences and cannot simply be subtracted. C treats the known incompatibility only downstream instead of first correcting the prompt/model interaction. D permanently rejects future improvement and is inconsistent with controlled lifecycle evolution.
The engineering principle is migration through regression testing and adaptation , not blind upgrading or permanent version avoidance.
Relevant Claude Developer topics: Systems Life Cycle, model migration, regression evaluation, prompt adaptation, compatibility testing, deployment gates, and continuous evolution .

NEW QUESTION # 28
You are configuring Claude Code for a new project. The team needs to set permissions, default model selections, and environment-specific behavior at the project level so the configuration is consistent across all developers working on the repository.
The Claude Code mechanism you would use is...

A. A shared spreadsheet that lists configuration values for team members to reference and update by hand as the project evolves.
B. Environment variables that each developer sets on their own machine when working with Claude Code on the project.
C. The settings.json file, scoped at the project level so the configuration applies consistently across developers and persists with the repository.
D. A system prompt embedded in every Claude Code conversation by each developer at the start of every session in the project.

Answer: C

Explanation:
D is directly aligned with Claude Code's configuration model. The supplied exam source marks D . Anthropic documents settings.json as the official mechanism for configuring Claude Code through hierarchical scopes.
Shared project configuration is stored in .claude/settings.json , which is intended to be checked into source control and shared with the team.
Project settings provide consistent repository-level behavior while still participating in Claude Code's configuration precedence. They can configure permissions, environment variables, tool behavior, and other supported project policies. Claude Code additionally distinguishes shared project settings from .claude
/settings.local.json, which is intentionally excluded from source control and is suitable for developer-specific experimentation or machine-local overrides.
A spreadsheet provides documentation but no executable configuration enforcement. B allows every developer's configuration to diverge and is appropriate only for values that genuinely belong to the local environment. C requires manual repetition and cannot reliably establish repository-wide policy.
Therefore, shared project settings.json is the appropriate configuration-as-code mechanism.
Relevant Claude Developer topics: Confia Management, Claude Code configuration, settings scopes, project settings, permissions, environment configuration, team consistency, and source-controlled configuration .

NEW QUESTION # 29
Your Claude application processes 50-page legal contracts and produces summaries with citation references back to the source. The team is debating whether to send each contract whole or split it into smaller pieces.
The contracts fit within Claude's context window. Initial testing shows that whole-document processing produces summaries with stronger cross-section reasoning but occasionally drifts on citation accuracy in later sections. Chunked processing produces stronger citation accuracy per chunk but loses cross-section reasoning.
The team has not decided which property matters more.
How would you guide the team's decision?

A. Identify which property matters more for the application's actual use case and let that decision drive the chunking approach, then validate the choice against representative contracts.
B. Review the citation accuracy results from chunked processing and determine whether the loss of cross- section reasoning produces summaries that still meet the application's quality bar.
C. Examine the use cases where cross-section reasoning failures occur and assess whether whole- document processing meets the application's accuracy requirements across a representative sample of contracts.
D. Assess the cost and latency implications of both approaches against the application's performance requirements before recommending which processing strategy to adopt.

Answer: A

Explanation:
B is correct because the architecture cannot be chosen intelligently until the team defines which quality attribute is more important to the actual business use case . The examination source explicitly selects B.
Whole-document and chunked processing each perform better on different dimensions, so there is no universally superior option.
Anthropic's evaluation guidance emphasizes defining concrete success criteria first and validating candidate approaches against representative examples. The correct sequence is therefore to determine whether cross- section reasoning or citation precision is the more critical requirement, establish measurable acceptance thresholds, and evaluate both strategies on representative legal contracts.
Anthropic's legal summarization guidance also confirms that chunking or meta-summarization is useful for long documents, while acknowledging that processing an entire document can be appropriate when it fits within Claude's context window. For citations specifically, Anthropic provides native citation support that chunks document text into citation-addressable units and improves source-grounded citation behavior.
A and C each privilege one quality dimension before establishing business priority. D focuses on cost and latency even though the unresolved problem is quality tradeoff.
Relevant Claude Developer topics: Understanding Reqs, success criteria, requirement prioritization, long- context processing, chunking, citation accuracy, cross-document reasoning, representative evaluations, and architecture tradeoffs .

NEW QUESTION # 30
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