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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

1. Your track_shipment(tracking_id) tool queries an external logistics API that sometimes fails - the API may be temporarily unavailable, the tracking ID may be malformed, or the shipment may not exist. Currently, your tool raises a Python exception when errors occur. Users report the agent gives unhelpful responses like "I'm having trouble with that request" instead of suggesting alternatives such as verifying the tracking number format or checking by order number. How should you handle errors in tool results?

A) Create dedicated error-recovery tools (retry_tracking_lookup, search_by_order_number) that the model can invoke after the primary tracking tool returns a failure indicator.
B) Return a generic error response (e.g., {"success": false, "error": "lookup_failed"}) for all failure cases to maintain a consistent schema and avoid exposing internal error details.
C) Return structured error information as normal tool output including error type, recoverability status, and actionable context for the user.
D) Implement retry logic with exponential backoff inside the tool implementation so transient errors are automatically handled and only return a result after all retry attempts are exhausted.

2. After integrating a local MCP server providing code analysis tools (analyze_dependencies, find_dead_code, calculate _complexity), you verify the server is healthy and tools appear in the tools/list response. However, you observe that the agent consistently uses Grep to search for import statements instead of calling analyze_dependencies -even when users explicitly ask about "code dependencies." Examining tool definitions reveals:
MCP: analyze_ dependencies - "Analyzes dependency graph"
Built-in: Grep - "Search file contents for a pattern using regular
expressions. Returns matching lines with line numbers and surrounding
context."
What's the most effective approach to improve the agent's selection of MCP tools?

A) Expand MCP tool descriptions to detail capabilities and outputs - e.g., "Builds dependency graph showing direct imports, transitive dependencies, and cycles."
B) Split analyze_dependencies into granular tools ( list_imports, resolve_transitive_deps, detect_circular_deps) so each has a focused purpose less likely to overlap with Grep.
C) Remove Grep from available tools when the MCP server is connected to eliminate functional overlap.
D) Add routing instructions to the system prompt specifying that dependency-related questions should use MCP tools rather than Grep.

3. A financial services company plans to integrate Claude into an internal document analysis platform. The architects want to minimize exposure of confidential client information while maintaining high-quality responses. Which approach BEST aligns with Anthropic's recommended architecture?

A) Remove unnecessary sensitive information before sending prompts whenever possible.
B) Replace Claude with a smaller local model regardless of task complexity.
C) Store every prompt permanently for auditing.
D) Send every document without preprocessing to maximize context.

4. Your code-review prompts include both implementation changes and the corresponding test file, but the review comments fail to identify untested code paths. The model correctly flags functions that have no tests at all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack coverage. What is the most effective way to improve branch- level gap detection without overcomplicating the pipeline?

A) Interleave the implementation and tests in the prompt, presenting each function immediately before its test cases.
B) Include few-shot examples showing code with an uncovered branch and the corresponding review comment identifying the missing test case.
C) Implement a two-pass pipeline in which one model call extracts all conditional branches and another cross-references them against test assertions.
D) Add explicit instructions requiring Claude to enumerate every conditional branch and exception path, then verify that each path has a corresponding test assertion.

5. During initial testing of the automated review pipeline, you notice that reviews of large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8-$12 per run because of extensive agentic loops--Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort after reaching either a fixed iteration count or a fixed dollar amount. Both limits must be enforced by Claude Code itself rather than by the surrounding job runner. Which configuration change directly enforces both per-invocation limits?

A) Use the --model flag to select a smaller, less expensive model so that every iteration uses fewer tokens and costs less.
B) Set --permission-mode dontAsk to automatically deny tool-permission requests that are not in the explicitly allowed set.
C) Add --max-turns 10 --max-budget-usd 2.00 to the claude -p invocation to cap agentic turns and expenditure.
D) Set timeout-minutes: 5 on the GitHub Actions step and monitor per-run costs through the Anthropic Console usage dashboard.

Solutions: