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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Tools and Model Context Protocol (MCP)	10.6%	- MCP server development - Tool integration and usage
Topic 2: Agents and Workflows	14.7%	- Agent architecture principles - Claude Agent SDK usage - Memory and context management - Workflow vs autonomous agents
Topic 3: Security and Safety	8.1%	- Guardrails and safety controls - AI application security
Topic 4: Model Selection and Optimization	16.8%	- Latency and performance trade-offs - Claude model family characteristics - Cost and token optimization
Topic 5: Claude Code	3.1%	- Claude Code configuration and usage
Topic 6: Prompt and Context Engineering	11%	- Context window management - Prompt design and structuring - Structured output handling
Topic 7: Evaluation, Testing, and Debugging	2.6%	- Output evaluation and validation - Error handling and debugging
Topic 8: Applications and Integration	33.1%	- Vision capabilities - Claude Messages API - Streaming and Batch API - SDK and third-party integration

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q29-Q34):

NEW QUESTION # 29
You are starting a new Claude application and have a small set of well-labeled examples that demonstrate the desired output format. You want to use these examples to guide Claude's behavior.
How would you guide the application's behavior?

A. Use multi-shot prompting by including the labeled examples in the prompt so Claude can match the desired output format on each request.
B. Embed the examples in a database and retrieve them at runtime as reference material for the team.
C. Use zero-shot prompting and rely on the model's general capability to produce the desired output format consistently across all incoming requests.
D. Train a custom model on the labeled examples before deployment so that the application does not need to include the examples in any prompt during operation.

Answer: A

Explanation:
The supplied examination set identifies A as correct. A small collection of high-quality labeled examples is ideally suited to few-shot or multishot prompting . The examples demonstrate concretely what acceptable input/output behavior looks like, allowing Claude to infer formatting, structure, tone, and task-specific conventions without requiring model retraining.
Anthropic's official prompting guidance states that examples are among the most reliable ways to steer Claude's output format, tone, and structure. It recommends using relevant, diverse examples and clearly separating them from the surrounding instructions. Anthropic currently recommends approximately three to five examples where practical and suggests XML structures such as < examples > and < example > to make prompt organization explicit.
B discards useful supervision by relying exclusively on zero-shot behavior. C is unnecessary for a small fixed set and does not ensure those examples are actually visible to Claude unless additional retrieval logic is created. D introduces unnecessary training complexity for a behavior that prompting already addresses efficiently.
Therefore, A provides the lowest-complexity, highest-leverage solution.
Relevant Claude Developer topics: Agent Construction, multishot prompting, few-shot learning, labeled examples, prompt design, output formatting, behavioral steering, and prompt evaluation .

NEW QUESTION # 30
Your Claude application runs long agentic workflows where the agent makes many tool calls, and the conversation history grows quickly. After about 20 tool calls, you notice the agent's responses become less focused and sometimes ignore earlier task constraints.
How would you address this?

A. Apply context engineering techniques such as tool output pruning or compaction to keep the active task state visible while reducing the volume of older content.
B. Restart the agent every five tool calls to prevent any drift, with the agent losing all task state at each restart point during the workflow.
C. Increase the model's context window so the agent can hold every tool output at full detail across the entire workflow no matter how many tool calls it accumulates.
D. Remove tool calling from the workflow entirely so the agent operates as a single text-generation step with no tool outputs accumulating in the context window.

Answer: A

Explanation:
Option B addresses the actual architectural failure: low-value historical material is crowding out the high- signal information required for current reasoning. A larger context window does not guarantee better attention to important constraints. Effective agent architecture actively manages what remains in context as the workflow progresses.
Anthropic's Context Editing documentation explicitly provides tool-result clearing for agentic workflows with heavy tool usage. Once Claude has processed an older result, verbose file contents, search output, or API responses may no longer need to remain in full. Older results can therefore be removed while recent and important state remains available. Anthropic also documents compaction, where accumulated history is summarized and replaced with a smaller representation that preserves important task state.
A removes an important agent capability rather than solving context growth. C retains all accumulated noise and merely gives it more space. D repeatedly destroys valuable task state and creates artificial workflow boundaries.
Therefore, B preserves goals, decisions, unresolved constraints, and necessary results while reducing irrelevant historical content. Relevant Study Guide topics: context engineering, tool-result clearing, compaction, long-running agents, context quality, and task-state preservation.

NEW QUESTION # 31
You are setting up a Claude application that will run a mix of multi-turn conversations and one-off requests.
You want to use caching techniques to reduce token costs where they apply. A teammate suggests caching the model's output as well, so the application does not have to make duplicate Claude calls when similar queries arrive.
You would apply prompt caching to...

A. The static portions of prompts that are repeated across requests, such as system prompts, instructions, or shared context.
B. Only the user's input portion of each request because user input is the part of the prompt that varies the most across the application's normal operation.
C. Nothing, because prompt caching does not affect cost in any application that mixes multi-turn conversations and one-off requests in a single deployment.
D. The model's output, treating the response from each request as cacheable content the application can return on similar future queries.

Answer: A

Explanation:
The supplied exam source marks D . Claude prompt caching is designed for repeated prompt prefixes, not semantic caching of generated answers. High-value cache candidates include stable system prompts, long instructions, tool definitions, shared background documents, repeated examples, and the previously accumulated prefix of a multi-turn conversation.
Anthropic explains that prompt caching reuses a matching prompt prefix and can substantially reduce processing time and input-token cost on subsequent requests. The cache operates across the request structure- tools, system content, and messages up to the relevant cache boundary. It is particularly useful for prompts with many examples, large repeated context, repetitive instructions, and long multi-turn conversations.
B describes response caching , which is a separate application-level technique and is not what Anthropic's prompt caching feature does. C targets the portion that usually changes most, making it a poor general cache boundary. A is incorrect because multi-turn workloads are a major prompt-caching use case.
Therefore, maximize reusable stable prefixes and place changing request-specific content after them where practical.
Relevant Claude Developer topics: Claude API Mechanics, prompt caching, cache prefixes, token-cost optimization, static context, system prompts, multi-turn conversations, and API efficiency .

NEW QUESTION # 32
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. 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.
B. Add deterministic output filtering that checks responses against the content policy before they reach users.
C. 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.
D. 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.

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 # 33
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. Increase the size of the agent's context window so it can hold the full ticket history at once.
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. Switch to a deterministic workflow that handles billing, shipping, and product issues in a fixed sequence.

Answer: C

NEW QUESTION # 34
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