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Anthropic CCAR-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q130-Q135):

NEW QUESTION # 130
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response ) in addition to tools.
How do these MCP prompts become accessible within Claude Code?

A. They are automatically prepended to every conversation as additional system-level context, influencing Claude's behavior throughout the session.
B. They appear as slash commands (e.g., /mcp__servername__deploy_checklist ) that you can invoke, with arguments passed after the command name.
C. They are surfaced as @ -mentionable resources alongside files, fetched and attached to your message when referenced.
D. They are added to Claude Code's tool registry alongside the server's tools, invoked automatically by the model when relevant to the task.

Answer: B

Explanation:
MCP prompts are exposed as user-invoked commands rather than autonomous tools or permanently loaded system instructions. Claude Code dynamically discovers prompts from connected MCP servers and displays them in the command list using the naming convention /mcp__servername__promptname .
Arguments are supplied as space-separated values after the command. When executed, the MCP server resolves the prompt and its returned content is injected into the active conversation. Anthropic's official documentation provides examples such as /mcp__github__list_prs and /mcp__jira__create_issue "Bug in login flow" high . ( https://code.claude.com/docs/en/mcp ) Option A would consume context continuously and incorrectly treat optional workflow templates as mandatory system instructions. Option B confuses MCP prompts with MCP tools: tools are model-callable operations, while prompts are reusable prompt templates invoked as commands. Option C describes MCP resources, which can be referenced and attached but are a distinct MCP capability.
For the stated server, the team could invoke commands such as /mcp__devops__deploy_checklist or
/mcp__devops__incident_response service-name . The exact server segment is derived from the configured server name, with normalization applied where necessary.
Official references/topics: MCP Prompts; Dynamic Prompt Discovery; MCP Slash-Command Naming; Prompt Arguments.

NEW QUESTION # 131
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
After deploying automated code review, developers report that approximately 35% of findings are false positives following consistent patterns: style suggestions that contradict team conventions, security warnings for patterns that are safe in the deployment environment, and performance suggestions that would degrade this particular use case.
You want to reduce false positives while enabling the model to generalize its judgment to novel code patterns it has not seen before.
Which approach is most effective?

A. Use keyword-based post-processing to remove findings containing terms such as "convention,"
"context-dependent," or "trade-off."
B. Include few-shot examples containing annotated code snippets that distinguish acceptable project patterns from genuine issues in each category.
C. Create a comprehensive specification of every pattern that must not be flagged and include the complete document in the system prompt.
D. Add general instructions telling Claude to be conservative and report only definite issues.

Answer: B

Explanation:
Option B demonstrates the decision boundary Claude must learn. Carefully selected examples can show structurally similar code producing different outcomes based on project context--for example, an approved authentication wrapper versus an unsafe direct call, or a deliberate performance trade- off versus an accidental quadratic operation. These contrasts help Claude apply the underlying judgment to new code rather than merely memorizing prohibited phrases.
Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable methods for improving output accuracy and consistency. It recommends several examples that mirror the real use case and cover important edge conditions.

NEW QUESTION # 132
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer asks your agent to identify untested code paths in a legacy payment processing module spanning
45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate-it' s forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows.
What's the most effective approach to complete this investigation?

A. Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration.
B. Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation.
C. Clear context with /clear , then selectively re-read only the most critical files discovered so far, writing key findings to a scratchpad file that persists between context resets.
D. Spawn subagents to investigate specific questions (e.g., "find all test files for payment processing,"
"trace refund flow dependencies") while the main agent coordinates findings and preserves high-level understanding.

Answer: D

Explanation:
The investigation contains several bounded research questions that can be delegated independently: locating the complete test suite, tracing payment and refund flows, identifying conditional branches, and mapping external dependencies. Each subagent can read the relevant files in its own context and return a focused summary to the coordinating agent.
Anthropic recommends subagents for codebase exploration because extensive file reading rapidly consumes the main context window. Subagents isolate that volume and return only their conclusions, preserving the main conversation for synthesis and implementation. ( https://docs.anthropic.com/en/docs/claude-code
/common-workflows ) Anthropic also describes parallel research as appropriate when separate investigation paths can proceed independently and the main agent can synthesize the results afterward. ( https://docs.
anthropic.com/en/docs/claude-code/sub-agents )
Option B sacrifices the current conversational state and requires reconstruction after /clear . Option C may reduce token usage, but isolated text matches cannot reliably reveal full execution paths, indirect calls, or test coverage relationships. Option D converts the current analysis into a single lossy summary and risks omitting details needed later.
Option A directly addresses the demonstrated context degradation while retaining a high-level coordinating thread. The subagent prompts should be narrowly scoped and require concrete outputs such as file paths, uncovered branches, call-chain evidence, and existing tests associated with each flow.
Official references/topics: Subagent Context Isolation; Parallel Research; Context Preservation; Coordinated Codebase Analysis.

NEW QUESTION # 133
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing. Which configuration approach correctly applies MCP server scopes?

A. Add venue server to .mcp.json and playlist server to ~/.claude.json.
B. Add both servers to your local ~/.claude.json
C. Add venue server to ~/.claude.json and playlist server to .mcp.json
D. Add both servers to the project-level .mcp.json file

Answer: A

Explanation:
Project-scoped MCP servers are stored in .mcp.json and can be shared through version control with the whole team. Personal local or user-scoped servers are stored in ~/.claude.json and remain private to the individual user.

NEW QUESTION # 134
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your system has been running for 3 weeks and human reviewers have corrected 847 extractions.
Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or
"a splash," the model either invents specific amounts or leaves fields empty--accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?

A. Fine-tune the model on the 847 corrected extractions.
B. Update your JSON schema to add a "measurement_type" enum field (precise/informal).
C. Add few-shot examples to your prompt demonstrating correct handling of informal measurements--extracting them verbatim rather than converting or omitting them.
D. Implement a post-processing layer that uses pattern matching to detect informal measurement phrases in source text and automatically populate values when the extraction is empty.

Answer: C

Explanation:
The reviewer corrections have exposed a narrow and repeatable interpretation failure. The desired policy is clear: informal measurements are valid source values and must be preserved verbatim rather than normalized into invented quantities or treated as missing. This behavior can be communicated efficiently through targeted few-shot examples.
Anthropic recommends examples for demonstrating expected behavior and improving consistency. Examples can pair source phrases such as "a handful of spinach," "a splash of vinegar," and "a pinch of salt" with outputs that retain handful, splash, and pinch exactly.
Additional counterexamples can show that Claude must not convert these phrases into grams, millilitres, or estimated serving quantities.

NEW QUESTION # 135
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