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Anthropic CCA-F Exam Syllabus Topics:

Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:

1. Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document analysis agent frequently identifies gaps - for instance, noting "the retrieved sources discuss API authentication but lack details on token refresh patterns" - but under the current strict pipeline, this insight isn't actionable since search has already completed. What's the most effective architectural change?

A) Add a research planning agent before the search phase that decomposes topics into specific sub- questions.
B) Have the coordinator review analysis output for gap indicators and re-invoke search with gap- informed queries when gaps are detected.
C) Have the analysis agent report specific gaps to the coordinator, which triggers targeted searches and re-invokes analysis until sufficient.
D) Have the synthesis agent attach confidence scores to each section and flag areas with insufficient coverage for manual review.

2. Your agent uses three tools: get_property_details(property_id) returns data including street address, get_price_history(property_id) returns historical pricing, and get_neighborhood_info(address) returns area statistics. You observe that get_neighborhood_info always requires get_property_details first just to extract the address, even when users specify the property by ID. This creates unnecessary latency and failure coupling - if the first call fails, the neighborhood request also fails. What tool design change best addresses this?

A) Change get_neighborhood_info to accept property_id, resolving the address internally.
B) Add retry logic and timeout handling to get_property_details.
C) Consolidate into a single get_property_with_neighborhood(property_id) tool returning both datasets.
D) Create a lookup_address(property_id) helper tool for retrieving addresses.

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 caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires. What's the most effective way to start your iterative workflow?

A) Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
B) Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.
C) Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.
D) Use plan mode to analyze the current/products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.

4. Claude produces occasional factual inaccuracies despite clear prompts. Which architectural enhancement is MOST effective?

A) Remove examples.
B) Add grounding through trusted retrieval.
C) Increase temperature.
D) Shorten responses only.

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

Solutions: