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Anthropic CCAR-F Exam Dumps Questions and Answers

Exam Code: CCAR-F
Exam Name: Claude Certified Architect – Foundations
Certification: Claude Certified Architect
Vendor: Anthropic
Last Update: Sep 1, 2026
Questions: 152 With Expert Explanation
10  Customers Passed Anthropic CCAR-F
86%  Average Success In Real Exam
90%  Questions came word by word from this dump
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Anthropic Related Certification Exams

Claude Certified Architect – Foundations Questions and Answers

Questions 1

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.

Your automated review jobs take 18 seconds to initialize before Claude begins analyzing code. Profiling reveals that the delay results from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUDE.md files throughout the monorepo.

You need to reduce startup time while ensuring reviews still enforce the coding standards documented in the root-level CLAUDE.md file.

What is the most effective approach?

Options:

A.

Replace the default prompt using --system-prompt-file ./CLAUDE.md, which bypasses default prompt assembly and loads only the project rules.

B.

Run in --bare mode and pass --append-system-prompt-file ./CLAUDE.md to load the required project standards explicitly while skipping automatic discovery.

C.

Run in --bare mode and repeat all review criteria directly in the -p prompt for every invocation.

D.

Keep the default initialization and add --exclude-dynamic-system-prompt-sections to improve prompt-cache reuse across CI runners.

Questions 2

You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.

Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn’t exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: { " isError " : true, " content " : [{ " type " : " text " , " text " : " Operation failed " }]} . The agent cannot distinguish between error types.

What’s the most effective improvement?

Options:

A.

Enhance error responses with structured metadata—include error_category (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.

B.

Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.

C.

Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.

D.

Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.

Questions 3

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 system has been operating with 100% human review for 3 months. Analysis shows that extractions with model confidence ≥90% have 97% accuracy overall. To reduce reviewer workload, you plan to automate high-confidence extractions.

Before deploying, what validation step is most critical?

Options:

A.

Analyze accuracy by document type and field to verify high-confidence extractions perform consistently across all segments, not just in aggregate.

B.

Compare accuracy at different confidence thresholds (85%, 90%, 95%) to find the optimal cutoff that maximizes automation while minimizing errors.

C.

Verify that 97% accuracy meets requirements for all downstream systems that consume the extracted data.

D.

Run a two-week pilot routing 25% of high-confidence extractions directly to downstream systems and monitor error reports.

Next Question