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

Question 2

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've documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What's the most efficient first diagnostic step?

A. Create path-specific rules in .claude/rules/handlers.md with YAML frontmatter scoping the error handling instructions to your API handler files.
B. Run /memory to check which memory files are loaded and verify your CLAUDE.md is included.
C. Search for conflicting instructions in ~/.claude/CLauDe.md or ~/.claude/rules/ that might override your project conventions.
D. Add a more detailed code examples to your CLAUDE.md showing the exact ApiError usage pattern for different endpoint types.

Question 3

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. 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.
B. Build post-processing keyword filters that suppress findings containing terms such as "force unwrap," "large class," or "deprecated import" before results reach developers.
C. Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.
D. 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.

Question 4

Your update_user_profile tool accepts a user_id (required) and an optional fields_to_update object. In testing, Claude frequently omits user_id or passes incorrectly structured data. What is most critical for helping Claude understand what parameter values to provide?

A. Verbose parameter names encoding format hints, such as user_id_string_uuid_format
B. Clear parameter descriptions explaining expected format, such as "user_id: UUID of the user to update (required)"
C. Detailed error responses explaining why invalid parameter values were rejected
D. Strict JSON Schema type constraints marking user_id as required and defining fields_to_update as an object type

Question 5

After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?

A. Pass the full accumulated context from all prior agents.
B. Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.
C. Pass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.
D. Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.

Solutions: