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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Tool Design & MCP Integration	18%	- Tool integration 1. Tool interface design 2. Model Context Protocol (MCP) 3. Tool selection and safety 4. Resource and server integration
Claude Code Configuration & Workflows	20%	- Claude Code 1. Development workflows 2. Agent skills 3. Code generation and automation 4. Configuration and project setup
Context Management & Reliability	15%	- Context handling 1. Reliability and evaluation 2. Context window management 3. Memory strategies 4. Cost and performance optimization
Prompt Engineering & Structured Output	20%	- Prompt design 1. Output validation 2. Prompt engineering techniques 3. Structured output and JSON schemas 4. Few-shot prompting
Agentic Architecture & Orchestration	27%	- Agentic architecture patterns 1. Agent orchestration 2. Workflow design 3. Planning and execution strategies 4. Single-agent and multi-agent architectures

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q35-Q40):

NEW QUESTION # 35
Your automated review CI jobs take 18 seconds to initialize before Claude begins analyzing code. Profiling reveals that the delay comes from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUDE.md files throughout your monorepo. You need to reduce startup time while ensuring that reviews still enforce your team's coding standards, which are documented in the root-level CLAUDE.md file.
What is the most effective approach?

A. Replace the default prompt entirely by using --system-prompt-file ./CLAUDE.md, which bypasses default prompt assembly and loads only your project rules.
B. Run in --bare mode and specify all review criteria directly in the -p prompt argument for every CI invocation, without referencing external files.
C. Run in --bare mode and pass --append-system-prompt-file ./CLAUDE.md to explicitly load your project standards while skipping all automatic discovery.
D. Keep the default initialization and add --exclude-dynamic-system-prompt-sections to reduce per- machine prompt variability and improve prompt-cache hit rates across runners.

Answer: C

Explanation:
Claude Code's --bare option is specifically designed for faster scripted execution. It skips automatic discovery of CLAUDE.md files, hooks, skills, plugins, MCP servers, and auto-memory while retaining essential built-in capabilities such as Bash, file reading, and file editing. Because --bare also prevents automatic loading of the root CLAUDE.md, the required standards must be added explicitly.
Option C accomplishes both objectives. --append-system-prompt-file ./CLAUDE.md loads the project standards into the current invocation while preserving Claude Code's default coding-agent system prompt and tool-use guidance. According to the official Claude Code CLI reference , append flags add file contents to the default system prompt, whereas replacement flags discard that default guidance.
Option A could work functionally but duplicates the standards in every command and creates configuration drift. Option B replaces the complete default prompt, removing useful coding, safety, and tool-use instructions. Option D improves prompt-cache reuse across different machines, but it does not disable hooks, plugins, skills, MCP servers, or CLAUDE.md discovery and therefore does not directly address the measured initialization delay.

NEW QUESTION # 36
Your infrastructure-as-code repository includes Terraform modules (/terraform/), Kubernetes manifests (/kubernetes/), and CI/CD pipeline scripts (/pipelines/). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?

A. Split content into subdirectory CLAUDE.md files (/terraform/CLAUDE.md,
/kubernetes/CLAUDE.md), so Claude loads directory-specific guidance.
B. Create files in .claude/rules/ with YAML frontmatter path-scoping (e.g., paths: ["terraform/**/*"]), loading rules only when editing matching files.
C. Keep the root CLAUDE.md and use @path/to/import syntax to modularly include tool-specific guidance files from separate documents.
D. Restructure the root CLAUDE.md into clearly labeled sections with headers (e.g, "## Terraform Conventions"), improving organization and readability.

Answer: B

Explanation:
The paths field conditionally loads each rule only when Claude works with matching files, preventing unrelated Terraform, Kubernetes, or pipeline guidance from consuming context.
Subdirectory CLAUDE.md files are directory-based, while path-scoped rules provide precise file- pattern targeting.

NEW QUESTION # 37
An engineering team notices that Claude occasionally provides answers beyond the company's internal policy documents. They want responses to rely only on approved documentation whenever possible. Which solution is MOST appropriate?

A. Increase temperature to 1.0
B. Use Retrieval-Augmented Generation (RAG)
C. Disable system prompts
D. Shorten every prompt

Answer: B

Explanation:
Retrieval-Augmented Generation allows Claude to access authoritative enterprise knowledge during inference. By grounding responses in retrieved documents, the model is less likely to rely on general knowledge or generate unsupported information. This improves factual accuracy and policy compliance.

NEW QUESTION # 38
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
The agent verifies customer identity through a multi-step process before resetting passwords. During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened.
What's the most likely cause of this behavior?

A. The conversation history isn't being passed in subsequent API requests.
B. The prompt lacks instructions telling Claude to remember information across multiple exchanges.
C. Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
D. The verification tool is clearing the agent's internal state after each successful validation step.

Answer: A

Explanation:
The Claude Messages API is stateless. Each API request must include the conversation history that Claude requires for the current response. If the application sends only the customer's third verification answer, Claude does not automatically retrieve the earlier turns containing the customer's name and previous answers.
From the model's perspective, that information is absent.
Anthropic's Messages API documentation states that applications must send the full conversational history to build a multi-turn interaction. The correct implementation appends each user message and assistant response to the messages array and resends the accumulated sequence on every subsequent request. ( https://platform.
claude.com/docs/en/build-with-claude/working-with-messages )
Option A cannot restore information that was omitted from the request. Instructions to "remember" do not create server-side conversational state. Option C describes application-specific behavior for which the scenario provides no evidence. Option D is factually incorrect: Claude does not have a default two-turn retention limit. Its effective conversational awareness depends on the messages provided and the model's context window.
For a verification workflow, the application should also persist structured verification state separately from prose history. That state may include completed checks, pending checks, attempt counts, and a verified customer identifier. This improves reliability while the conversational transcript preserves the natural interaction.
Official references/topics: Stateless Messages API, multi-turn history construction, structured workflow state, conversational continuity.

NEW QUESTION # 39
Compliance requires that refunds exceeding $500 must automatically escalate to a human agent
- this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate). How should you achieve guaranteed compliance?

A. Strengthen the system prompt with emphatic language: "CRITICAL POLICY: Refunds over $500 MUST trigger human escalation. NEVER process these directly."
B. Implement a hook to intercept tool calls; when the refund process amount exceeds $500, block it and invoke human escalation.
C. Modify the refund tool to return an error with message "Amount exceeds policy limit - please escalate" when threshold is exceeded.
D. Add few-shot examples to the prompt showing correct escalation behavior at various refund amounts ($400, $500, $600).

Answer: B

Explanation:
Enforcing the policy outside of the model via a PreToolUse hook guarantees compliance. By intercepting high-value refund attempts and triggering human escalation, the system ensures the rule is applied consistently regardless of prompt instructions or agent behavior.

NEW QUESTION # 40
......

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