Anthropic CCA-F Test Passed : Claude Certified Architect Foundations (CCA-F)

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Anthropic CCA-F Exam Syllabus Topics:

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

1. 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 need to add a date validation check ensuring event dates are in the future. This requires adding a conditional statement to one existing function in a single file. What is the most appropriate approach?

A) Use direct execution to make the change.
B) Enter plan mode first to create a detailed implementation strategy before making the change.
C) Start with extended thinking mode enabled to ensure thorough reasoning about the validation logic.
D) Enter plan mode to analyze how the validation might impact other parts of the reservation flow.

2. Your multi-agent research pipeline crashed after processing12 of 28 documents. The web search agent had identified relevant sources, the document analyzer had partially completed extraction, and the synthesizer had begun pattern identification. You need to resume processing without repeating work or losing fidelity of prior findings. What state management approach best balances information fidelity with context efficiency when restoring agent state?

A) Have each agent persist a structured export to a known location. On resume, the coordinator loads the manifest and injects relevant state into agent prompts.
B) Index all agent outputs in a shared vector store. When resuming, each agent queries the store using semantic search to retrieve relevant prior findings.
C) Have each agent maintain its own persistent state file and reload it independently at the start of each session.
D) Persist the coordinator's conversation log containing all task delegations and responses, providing this to agents when resuming.

3. After 30+ turns, your conversational assistant shows noticeably slower responses and occasionally produces less coherent outputs. Investigation reveals: (1) average conversations reach 50,000 tokens by turn 35, (2) production logs show 94% of user messages only reference the previous 3-5 exchanges, (3) the 6% of queries referencing earlier context typically ask about information the user could easily re-state. Your goal is to improve response speed and quality while maintaining good user experience. What's the most effective approach?

A) Enable prompt caching and continue sending the complete conversation history, using cached prefixes to reduce per-request costs while preserving all context.
B) Implement a sliding window keeping only the system prompt and last 8-10 turns. When users reference earlier context, acknowledge the limitation and ask them to re-state the relevant information.
C) Implement a summarization layer that progressively compresses older conversation turns into a running summary while keeping the most recent 5-6 turns verbatim, maintaining full historical context in condensed form.
D) Build a retrieval system that stores all conversation turns and uses semantic search to pull in relevant historical context only when the current query appears to reference past information.

4. During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process _refund immediately - but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist. Which change directly addresses the root cause of the agent fabricating the order_id value?

A) Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.
B) Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.
C) Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
D) Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.

5. You've configured the system so that all four subagents have access to the complete set of
18 tools. During testing, agents frequently call tools outside their specialization - the synthesis agent attempts web searches, and the report generator tries to analyze documents. What is the primary cause of this poor tool selection behavior?

A) The agents' role descriptions in their system prompts conflict with having access to tools outside that role.
B) The tool definitions consume too much context window space, leaving insufficient room for task content.
C) Choosing from 18 tools instead of 4-5 relevant ones increases decision complexity beyond reliable selection thresholds.
D) The coordinator cannot track which capabilities each subagent has, leading to misrouted tasks.

Solutions: