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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

The coordinator provides detailed step-by-step instructions to the web-search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues:
(1) the subagent reports "insufficient results" instead of trying
alternative approaches when the specified searches fail,
(2) research quality drops for emerging topics that do not match
expected patterns, and
(3) the subagent rarely surfaces valuable tangential sources.
What is the most effective way to improve subagent adaptability?

A. Remove procedural details entirely and delegate using simple goals such as "research this topic thoroughly," relying on the subagent's general capabilities.
B. Classify each topic as either "well-defined" or "exploratory" and use a different instruction style for each category.
C. Specify research objectives and quality criteria--such as coverage breadth, source diversity, and recency--rather than prescribing procedural steps, allowing the subagent to determine its search strategy.
D. Add fallback directives requiring alternative query formulations whenever the specified searches produce fewer than a predetermined number of results.

Question 2

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
After implementing tool use with strict schema definitions, JSON syntax errors are eliminated, but
5% of extractions still contain empty arrays or null values for required fields such as citations and methodology. Spot-checking reveals that the source documents contain this information, but in varied formats--inline citations versus bibliographies, and methodology sections versus details embedded in introductions.
What is the most effective way to address these failures?

A. Build a regex-based post-processing layer that scans source documents for citation patterns and methodology keywords, populating empty fields when the model fails to extract them.
B. Implement retry logic that resends requests when validation detects empty required fields.
C. Modify the schema to make citations and methodology optional, and flag incomplete records for manual review instead of failing validation.
D. Add few-shot examples demonstrating extractions from documents with varied structures, showing how to identify citations in different formats and locate methodology details across section types.

Question 3

You are building developer-productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools--Read, Write, Bash, Grep, and Glob--and integrates with Model Context Protocol (MCP) servers.
Engineers frequently ask the agent to cross-reference code changes with Jira tickets during reviews--checking ticket descriptions, acceptance criteria, and recent comments. This currently requires manually copying and pasting content into conversations. The team wants the agent to access this standard Jira ticket data directly.
What is the most effective approach?

A. Integrate an existing Jira MCP server that exposes tickets, comments, and metadata through discoverable tool interfaces.
B. Use the Bash tool with curl to call Jira's REST API, including authentication headers and parsing JSON responses inline.
C. Build a custom MCP server wrapping Jira's API with tools designed specifically for this team's code-review workflow.
D. Export Jira tickets to Markdown files in the repository that the agent accesses using the Read tool.

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

Question 5

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.
The system needs to extract candidate information (name, contact details, skills, work experience, education) from uploaded resumes. The extracted data must strictly conform to a predefined JSON schema, as missing required fields or incorrect data types will cause downstream validation failures.
What is the most reliable approach to ensure Claude's output consistently matches the schema?

A. Make two separate API calls--first extracting information as text, then asking Claude to format that text as JSON.
B. Define a tool with an input schema matching your required JSON structure and extract the data from Claude's tool_use response.
C. Parse Claude's text response with regex patterns to extract JSON objects, using retry logic for malformed responses.
D. Include detailed JSON formatting instructions and a template example in the system prompt, asking Claude to output only valid JSON.

Solutions: