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Anthropic CCAR-F Exam Syllabus Topics:

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 submits two requests:
* Request A: "Rename the getUserData function to fetchUserProfile everywhere it's used."
* Request B: "Improve error handling throughout the data processing module-add try/catch blocks, meaningful error messages, and ensure failures don't silently corrupt data." For which request does specifying an explicit multi-phase workflow (such as analyze # propose # implement with review) most improve outcome quality?

A) Both requests benefit equally
B) Request B, the error handling task
C) Neither request benefits significantly
D) Request A, the function rename task

2. 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.
A developer asks the agent to investigate why a specific API endpoint intermittently returns 500 errors. The codebase has 200+ files and the developer doesn't know which components are involved. The agent must trace the error through routing, middleware, business logic, and database layers.
What task decomposition approach would be most effective?

A) Run parallel worker agents that simultaneously investigate all four layers, then synthesize their findings to identify where the error originates.
B) Have the agent dynamically generate investigation subtasks based on what it discovers at each step, adapting its exploration plan as new information about the error path emerges.
C) Have the agent first create a comprehensive plan mapping all code paths through the endpoint before beginning any file exploration or code reading.
D) Define a fixed sequence of investigation steps upfront-grep for error patterns, then read error handlers, then check database queries, then examine middleware-executing each step regardless of intermediate findings.

3. 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) Add prompt instructions to return null for any field where information is not directly stated in the source.
B) Upgrade to a more capable model tier with improved instruction-following to reduce hallucination tendencies.
C) Make all schema fields required (non-nullable) with strict validation rules to ensure the model only outputs verifiable data.
D) Add a post-processing step using a second LLM call to verify each extracted value exists in the source document.

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.
Your codebase exploration tool stores session IDs to allow engineers to continue investigations across work sessions. An engineer spent an hour yesterday analyzing a legacy authentication module, building context about its architecture and dependencies. They want to continue today. The session ID is valid, but version control shows 3 of the 12 files the agent previously read were modified overnight by a teammate's merge.
What approach best balances efficiency and accuracy?

A) Resume the session and inform the agent which specific files changed for targeted re-analysis
B) Start a fresh session to ensure the agent works with current codebase state without stale assumptions
C) Resume the session without informing the agent about the changed files
D) Resume the session and immediately have the agent re-read all 12 previously analyzed files

5. 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.
A security audit requires updating your authentication library from v2 to v3. The migration guide documents breaking changes: authenticate() now returns a Promise instead of accepting a callback, the User type has restructured fields, and three deprecated methods were removed. Grep shows the library is imported in 45 files across several modules.
What's the most effective approach?

A) Create a custom slash command encapsulating the migration transformations, then execute it against each file without prior codebase exploration.
B) Update the dependency version, run the test suite, and use Claude Code to fix each failure as it appears.
C) Enter plan mode to explore library usage across modules, map affected code paths, then create a migration strategy before implementing.
D) Paste the migration guide's breaking changes into your prompt and use direct execution to update all usages across the 45 files.

Solutions: