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Anthropic CCAR-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Structured output generation and validation - Improving Claude response quality and consistency - Prompt design strategies |
| Context Management & Reliability | 15% | - Production deployment considerations - Managing context windows and information flow - Evaluation and reliability strategies |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications - Tool safety, reliability, and usability |
| Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Integrating Claude Code into development processes - Developer productivity workflows |
| Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Designing agentic systems and workflows - Selecting appropriate Claude architectures |
Anthropic Claude Certified Architect – Foundations Sample Questions:
1. 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 used Claude Code yesterday to investigate authentication flows in a legacy monolith, building up significant context over a 2-hour session. Today she wants to continue that specific investigation. She's worked on three other codebases since then and knows the session was named "auth-deep-dive".
How should she resume?
A) Use --continue to pick up where the most recent conversation left off
B) Use --resume auth-deep-dive to load that specific session by name
C) Use --session-id with the UUID from yesterday's session transcript file
D) Start fresh and re-read the same files
2. 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.
You've asked Claude Code to build a PDF report generation feature. The initial implementation queries the database correctly, but the output has formatting issues: table columns are too narrow causing content truncation, dates display without proper formatting, and page break handling is incorrect. You've noticed these issues interact-changing column widths affects how dates render, and page breaks depend on content height.
What's the most effective approach for iterating toward a working solution?
A) Show Claude an example of a correctly formatted report and ask it to match that output, rather than listing the specific technical issues.
B) Address the column width issue first with specific measurements, verify it works, then fix date formatting within the corrected columns, then adjust page breaks-testing after each change.
C) Start fresh with a detailed prompt specifying all formatting requirements upfront.
D) Provide all three issues in a single detailed message with exact specifications for each, allowing Claude to address them together in one update.
3. 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's exploration subagent spent 30 minutes analyzing a legacy payment system, reading 47 files and documenting data flows. The session was interrupted when the engineer's connection dropped. While away, a teammate merged a PR that renamed two utility functions. The engineer wants to continue the same exploration.
What's the most effective approach?
A) Resume the subagent from its previous transcript without mentioning the changes-the architecture understanding remains valid.
B) Launch a fresh subagent with a summary of prior findings.
C) Resume the subagent from its previous transcript and inform it about the renamed functions.
D) Launch a fresh subagent and include the prior transcript in the initial prompt for context.
4. 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) 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.
B) 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.
C) Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration.
D) Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation.
5. 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 extracts event metadata (date, location, organizer, attendee_count) from news articles using a JSON schema with all nullable fields. During evaluation, you observe the model frequently generates plausible but incorrect values for fields not mentioned in the article-for example, outputting "500" for attendee_count when the source contains no attendance information.
What's the most effective way to reduce these false extractions?
A) Make all schema fields required (non-nullable) with strict validation rules to ensure the model only outputs verifiable data.
B) Upgrade to a more capable model tier with improved instruction-following to reduce hallucination tendencies.
C) Add a post-processing step using a second LLM call to verify each extracted value exists in the source document.
D) Add prompt instructions to return null for any field where information is not directly stated in the source.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: D |
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