Claude Certified Architect — Foundations (CCA-F)

Đề thi thử (120 câu)

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1. Agentic Architecture Production data shows that in 12% of cases, your agent skips get_customer entirely and calls lookup_order using only the customer's stated name, occasionally leading to misidentified accounts and incorrect refunds. What change would most effectively address this reliability issue?

   • A. Add a programmatic prerequisite that blocks lookup_order and process_refund calls until get_customer has returned a verified customer ID.
   • B. Enhance the system prompt to state that customer verification via get_customer is mandatory before any order operations.
   • C. Add few-shot examples showing the agent always calling get_customer first, even when customers volunteer order details.
   • D. Implement a routing classifier that analyzes each request and enables only the subset of tools appropriate for that request type.

2. Agentic Architecture Your system integrates multiple backend APIs that return timestamps in different formats (Unix epochs, ISO 8601 strings, and custom database formats). You want the agent to receive clean, uniform dates without wasting its context window on parsing logic. Where should you implement this normalization?

   • A. Inside the system prompt, instructing the model to normalize all timestamps before reasoning.
   • B. Inside a PostToolUse hook of the Agent SDK, intercepting the tool output to format the data before it is appended to the message history.
   • C. By adding few-shot examples showing the agent converting various formats to ISO 8601.
   • D. By setting tool_choice to force the agent to call a custom DateFormatter tool after every API request.

3. Agentic Architecture In a customer support agent, a developer hard-coded a fixed tool sequence: the loop always calls get_customer, then lookup_order, then process_refund on every request. This misbehaves on requests that need none of those steps (e.g., a simple address change). What is the most appropriate redesign?

   • A. Remove the hard-coded sequence and let the model choose the next tool each iteration based on the evolving conversation context, feeding tool results back so it can reason about the next action.
   • B. Expand the decision tree with additional conditional branches to cover address changes, billing questions, and every other request type.
   • C. Deploy a separate intent-classification model that maps each incoming request to one of several predefined tool sequences before the agent runs.
   • D. Switch to a larger model so the fixed tool sequence produces better results on edge-case requests.

4. Agentic Architecture In your multi-agent research pipeline, the synthesis agent produces reports whose claims can no longer be traced back to their sources because source URLs and publication dates are lost when the web search agent's output is handed off. How should you pass context to preserve attribution?

   • A. Pass each finding as structured data that separates the claim/content from its metadata (source URL, document name, publication date) so the synthesis agent preserves attribution through synthesis.
   • B. Concatenate all raw search result text into a single plaintext blob and let the synthesis agent re-derive the sources itself.
   • C. Enable context inheritance so the synthesis agent automatically receives the web search agent's full history including sources.
   • D. Give the synthesis agent a larger context window so more of the original source text fits alongside the claims.

5. Agentic Architecture You built a codebase-exploration agent with the Claude Agent SDK. To stop the agentic loop you set a hard limit of 8 iterations as the primary termination mechanism. Complex investigations now get cut off before finishing, while trivial ones still run all 8 rounds. What is the correct way to control termination?

   • A. Raise the hard iteration cap to 50 so complex investigations have room to finish.
   • B. Parse the assistant's text each round and stop the loop once it contains a phrase such as 'investigation complete'.
   • C. Drive termination from stop_reason — continue while it is 'tool_use' and stop when it is 'end_turn' — keeping an iteration cap only as a secondary safety backstop.
   • D. Stop the loop as soon as a response contains any assistant text content alongside tool calls.

6. Agentic Architecture To reduce latency, a developer wired the subagents in your research system to call one another directly instead of returning to the coordinator between steps. Now error handling is inconsistent and it is hard to trace how information flowed. What architectural principle should guide the redesign?

   • A. Keep the peer-to-peer calls but add a shared logging database so every direct call is recorded for observability.
   • B. Give each subagent the full tool set so it can complete any downstream step itself without calling a peer.
   • C. Have every subagent broadcast its output to a shared global-memory bus that the others poll.
   • D. Route all inter-subagent communication through the coordinator, which manages delegation, result routing, and consistent error handling.

7. Agentic Architecture Logs show your returns agent sometimes calls issue_store_credit before it has run validate_return_eligibility, occasionally granting credit for items outside the return window. A strongly worded system-prompt rule has not eliminated the problem. What most reliably enforces the required ordering?

   • A. Add few-shot examples showing the agent always validating eligibility before issuing credit.
   • B. Rephrase the system-prompt rule more forcefully and place it at the top of the prompt.
   • C. Add an intent classifier that only enables issue_store_credit for requests it labels as valid returns.
   • D. Implement a programmatic prerequisite gate that blocks issue_store_credit until validate_return_eligibility has returned an eligible result.

8. Agentic Architecture Your CI code review must always examine every pull request for the same three predetermined aspects — security, performance, and style — and a single combined pass produces shallow, inconsistent results. Which task-decomposition strategy fits this predictable, multi-aspect review?

   • A. Use a fixed prompt chain that runs a focused pass for each aspect in sequence (security, then performance, then style).
   • B. Use adaptive decomposition that generates new review aspects on the fly based on what each pass discovers.
   • C. Run one pass over the whole PR with a larger-context model so all three aspects fit at once.
   • D. Spawn one subagent per changed line and merge their raw outputs.

9. Tool Design & MCP Production logs show the agent frequently calls get_customer when users ask about orders (e.g., 'check my order #12345'), instead of calling lookup_order. Both tools have minimal descriptions ('Retrieves customer information' / 'Retrieves order details') and accept similar identifier formats. What's the most effective first step to improve tool selection reliability?

   • A. Add few-shot examples to the system prompt demonstrating correct tool selection patterns, with 5-8 examples showing order-related queries routing to lookup_order.
   • B. Expand each tool's description to include input formats it handles, example queries, edge cases, and boundaries explaining when to use it versus similar tools.
   • C. Implement a routing layer that parses user input before each turn and pre-selects the appropriate tool based on detected keywords and identifier patterns.
   • D. Consolidate both tools into a single lookup_entity tool that accepts any identifier and internally determines which backend to query.

10. Tool Design & MCP You want Claude Code to find all occurrences of a specific import statement in a multi-directory project. Which built-in tool should you instruct the agent to use?

    • A. Glob
    • B. Grep
    • C. Read
    • D. Bash

11. Tool Design & MCP Your extraction pipeline must guarantee that extract_metadata runs first — before any enrichment tools — as a single deterministic step at the start of the turn. Which tool_choice configuration achieves exactly this?

    • A. tool_choice: "auto", relying on the model to usually pick extract_metadata first.
    • B. tool_choice: "any", so the model is guaranteed to call a tool but chooses which one.
    • C. tool_choice: {"type": "tool", "name": "extract_metadata"} to force that specific tool, then process enrichment in follow-up turns.
    • D. tool_choice: "required" with a priority field listing extract_metadata first.

12. Tool Design & MCP Your support agent's process_refund MCP tool returns a generic 'Operation failed' whenever a refund is rejected because it exceeds the customer's eligible amount (a policy violation). In production the agent keeps calling process_refund again and again on these rejections, wasting calls and never explaining the outcome to the customer. How should the tool structure this failure?

    • A. Return isError: true with errorCategory: transient and isRetryable: true so the agent retries until the refund eventually goes through.
    • B. Return isError: true with errorCategory: business, isRetryable: false, and a customer-friendly explanation so the agent stops retrying and communicates or escalates appropriately.
    • C. Return an empty successful result so the agent treats the refund as done and moves on.
    • D. Throw a terminal exception in the MCP server so the whole session restarts and clears the failed attempt.

13. Tool Design & MCP A developer on your team wants to try an experimental personal MCP server on their own machine while iterating on it. It should not affect teammates and must not be committed to the shared repo configuration. Where should they configure it?

    • A. In the project-scoped .mcp.json so it is discovered by everyone who clones the repo.
    • B. In the user-scoped ~/.claude.json, so the server stays personal and is not shared via version control.
    • C. Under an MCP section inside the project root CLAUDE.md so the agent loads it as context.
    • D. In a .claude/mcp-local.json file that Claude Code automatically git-ignores for personal servers.

14. Claude Code Configuration A new developer joins your team and notices that Claude Code is not following the project's standard naming conventions, despite other team members having no issues. What is the most likely cause of this discrepancy?

    • A. The naming conventions were defined in the user-level configuration (~/.claude/CLAUDE.md) of the existing developers instead of the project-level root.
    • B. The new developer forgot to run the /memory command to index the project's folder hierarchy.
    • C. The project is using glob-scoped rules which are blocked by default on new installations.
    • D. The naming conventions were set inside a directory-level CLAUDE.md file that the agent cannot access.

15. Claude Code Configuration You want Claude Code to implement a complex caching mechanism. To ensure the implementation covers edge cases like cache stampede and failure modes, what technique should you use before coding begins?

    • A. Provide a monolithic system prompt listing all cache stampede failure scenarios.
    • B. Use the interview pattern by instructing Claude to ask clarifying questions about invalidation, TTL, and failure modes before writing code.
    • C. Run the task in direct execution mode and iterate based on compiler error logs.
    • D. Create a custom skill with allowed-tools restricted to 'Read' and 'Grep'.

16. Claude Code Configuration Your platform has dozens of microservice directories, and every service exposes its entry point in a file named handler.ts. You need Claude to apply the same error-handling and logging conventions to all of these handler files, no matter which service directory they live in, without maintaining config in every folder. What is the most maintainable configuration?

    • A. Put all handler conventions under a heading in the root CLAUDE.md and rely on the agent to infer when a file is a handler.
    • B. Place a CLAUDE.md file inside each microservice directory containing the handler conventions.
    • C. Store the conventions in a .claude/config.json file keyed by the filename handler.ts.
    • D. Create a file in .claude/rules/ with YAML frontmatter paths: ["**/handler.ts"] so the conventions load automatically whenever a handler file is edited, regardless of directory.

17. Claude Code Configuration A developer reports that Claude Code behaves inconsistently between sessions — sometimes it follows a specific project rule and sometimes it ignores it. You suspect a memory file is not loading reliably. What is the correct way to diagnose which memory files are actually in effect?

    • A. Run /memory to view which memory files are currently loaded and verify the rule's source file is among them.
    • B. Run /memory to re-index the project's folders so every CLAUDE.md is reloaded into memory.
    • C. Add the rule to ~/.claude/CLAUDE.md so it loads globally and overrides the session inconsistencies.
    • D. Increase max_tokens so the full CLAUDE.md always fits in context on every session.

18. Claude Code Configuration Your repository contains Terraform infrastructure code, and every .tf file must follow strict tagging and naming conventions. These Terraform files are spread across many different directories. What is the most maintainable way to ensure Claude applies these conventions only when editing Terraform files?

    • A. Place a CLAUDE.md inside each directory that contains Terraform files.
    • B. Create a .claude/rules/ file with YAML frontmatter paths: ["**/*.tf"] so the conventions load only when editing Terraform files.
    • C. Put the Terraform conventions in the root CLAUDE.md and rely on the agent to infer when a file is Terraform.
    • D. Register the conventions in a .claude/config.json keyed by the .tf extension.

19. Claude Code Configuration You have described a data-normalization transformation to Claude in prose several times, but it keeps interpreting the output format inconsistently across records. What is the most effective way to communicate the exact transformation you expect?

    • A. Rewrite the prose description with more forceful, detailed wording until it is unambiguous.
    • B. Provide 2-3 concrete input/output examples that demonstrate the exact transformation you expect.
    • C. Use the interview pattern so Claude asks clarifying questions about the format before transforming.
    • D. Lower the model temperature so the output format stays consistent across records.

20. Prompt & Structured Output Your automated code review prompt flags standard console logs as high-severity security vulnerabilities. Developers are losing trust in the tool. How should you address this issue?

    • A. Add general instructions to the prompt such as 'be conservative' and 'only report high-confidence findings'.
    • B. Define explicit review criteria with concrete code examples distinguishing acceptable log patterns from genuine vulnerabilities.
    • C. Switch to a larger, more expensive model to improve overall reasoning capabilities.
    • D. Configure tool_choice to force the review agent to use the Bash tool to inspect log directories.

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