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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

1. Your MCP server includes archive_file(file_id) and delete_file(file_id) tools. Production logs show the agent calls delete_file when users ask to "remove old backups," but company policy requires archiving backup files. Both tools currently have minimal descriptions: "Archives a file" and
"Deletes a file." Which change most directly improves tool selection for this scenario?

A) Expand tool descriptions to clarify use cases, adding guidance like "Do not use for backup files" to delete_file.
B) Implement server-side validation that rejects delete_file calls for files tagged as backups, returning an error message suggesting archive_file.
C) Add a confirmation step that requires users to type "CONFIRM DELETE" before delete_file executes.
D) Add few-shot examples to the system prompt demonstrating that requests involving "backup" or
"old" should use archive_file.

2. A team wants to reduce hallucinations without significantly changing application logic. Which improvement should be implemented FIRST?

A) Remove role instructions.
B) Raise temperature.
C) Add reliable retrieval sources.
D) Shorten all outputs.

3. 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're implementing a complex graph traversal algorithm with specific performance requirements and edge cases to handle (disconnected nodes, cycles, weighted edges). You want to structure your workflow for efficient iterative refinement with Claude. What approach will most effectively enable progressive improvement across multiple iterations?

A) Have Claude extensively research the algorithm and create a detailed implementation plan using extended thinking, then implement the complete solution based on that plan.
B) Provide Claude with a detailed natural language specification of the algorithm, including all requirements and edge cases. Review each output manually and provide descriptive feedback on what behavior needs to change.
C) Write a test suite covering expected behavior, edge cases, and performance requirements before implementation. Ask Claude to write code that passes the tests, then iterate by sharing test failures with each refinement request.
D) Provide Claude with a reference implementation from documentation, then ask it to rewrite the code to match your codebase style and add the required edge case handling, comparing outputs against the reference.

4. You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement, losing important nuance, while other reports use excessive qualifications and become unhelpful. The web-search agent returns, "Industry analysts estimate a $50 billion market size, although methodologies vary." The document- analysis agent returns, "A peer-reviewed study estimates $35 billion, with a ?7 billion 95% confidence interval." The coordinator either selects one estimate arbitrarily or produces a vague $35?50 billion range.
What systematic approach best addresses this?

A) Add a verification subagent that passes claims to synthesis only when they are corroborated by at least two independent sources.
B) Configure subagents to report only findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.
C) Instruct the synthesis agent to distinguish well-established findings from contested findings explicitly, preserving each source's original uncertainty, methodology, and supporting evidence.
D) Implement a confidence-calibration layer that normalizes subagent uncertainty expressions to probability scores between 0.0 and 1.0, and then calculate a confidence-weighted average.

5. 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 the agent to understand how the caching layer works before adding a new cache invalidation trigger. After initial Grep searches, the agent has identified that caching logic spans 15 files including decorators, middleware, and service classes (~8,000 lines total). What's the most effective next step for building understanding while managing context constraints?

A) Use the Read tool to sequentially load all 15 files, building complete understanding across the full caching implementation.
B) Analyze imports and class hierarchies to identify the base cache class, Read that file to understand the interface, then trace specific invalidation implementations.
C) Use Grep to search for "invalidate" and "expire" patterns across all files, then Read only those specific line ranges with minimal surrounding context.
D) Use Glob to find files matching common caching patterns (cache.py, caching/), prioritize the largest files by reading them first, then check smaller files for gaps.

Solutions: