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Anthropic CCAR-F Exam Overview:

Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

The automated review consistently flags patterns your team uses intentionally--force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK.
Developers are dismissing approximately 30% of all findings as project-specific false positives.
Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?

A. Build post-processing keyword filters that suppress findings containing terms such as "force unwrap," "large class," or "deprecated import" before results reach developers.
B. Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.
C. Document the team's accepted patterns and intentional conventions in the project's CLAUDE.md file so the model receives this context during every review.
D. Configure the review to analyze only the changed lines in the diff without surrounding file context, reducing the amount of code the model evaluates during each review.

Question 2

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your pipeline uses a tool called extract_metadata with a JSON schema for paper details. You've also defined lookup_citations and verify_doi tools for enrichment. During testing, you notice that when users include requests like "extract the metadata and tell me how cited it is," Claude sometimes calls lookup_citations first, which fails because it needs the DOI that extract_metadata would provide.
What's the most effective way to ensure structured metadata extraction happens first?

A. Set tool_choice to {"type": "tool", "name": "extract_metadata"} and process the enrichment requests in subsequent turns after receiving the extracted metadata.
B. Set tool_choice to {"type": "tool", "name": "extract_metadata"} for every API call in the pipeline, ensuring Claude always extracts metadata before any enrichment can occur.
C. Set tool_choice to "any" so Claude must use a tool, combined with system prompt instructions prioritizing extract_metadata.
D. Set tool_choice to "auto" and reorder the tool definitions so extract_metadata appears first in the tools array, since Claude prioritizes earlier-listed tools.

Question 3

Your agent has called lookup_order multiple times while investigating a customer's return requests. Each response includes 40+ fields (items, shipping details, payment info, status history). Tool outputs now represent the majority of the conversation's context. The customer mentions two more orders they want to discuss. What's the most effective approach before making additional lookups?

A. Move all tool responses to a vector database with semantic indexing, retrieving relevant portions as the conversation continues
B. Extract only the return-relevant fields (items, purchase date, return window, status) from each existing order response, removing verbose details
C. Proceed with additional lookups without modifying the existing tool output context
D. Have the model generate a natural language summary of each order's key details, replacing structured responses with prose descriptions

Question 4

The agent verifies customer identity through a multi-step process before resetting passwords.
During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened. What's the most likely cause of this behavior?

A. Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
B. The prompt lacks instructions telling Claude to remember information across multiple exchanges.
C. The conversation history isn't being passed in subsequent API requests.
D. The verification tool is clearing the agent's internal state after each successful validation step.

Question 5

A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API. What is the primary mechanism your application uses to determine whether to continue the loop or stop?

A. You check the stop_reason field in each API response - the loop continues while it equals
"tool_use" and exits when it changes to "end_turn" or another terminal value.
B. You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.
C. You check whether Claude's response contains a text content block - if text is present, the agent has produced its final answer and the loop should exit.
D. You manually set the tool_choice parameter to "none" after the final expected tool call to force Claude to stop requesting tools.

Solutions: