Pass exam with CCAR-F Top Exam Collection for sure one-shot

Anthropic CCAR-F Exam Overview:

Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 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.
After deploying automated code review, developers report that approximately 35% of findings are false positives following consistent patterns: style suggestions that contradict team conventions, security warnings for patterns that are safe in the deployment environment, and performance suggestions that would degrade this particular use case.
You want to reduce false positives while enabling the model to generalize its judgment to novel code patterns it has not seen before.
Which approach is most effective?

A. Include few-shot examples containing annotated code snippets that distinguish acceptable project patterns from genuine issues in each category.
B. Add general instructions telling Claude to be conservative and report only definite issues.
C. Create a comprehensive specification of every pattern that must not be flagged and include the complete document in the system prompt.
D. Use keyword-based post-processing to remove findings containing terms such as "convention,"
"context-dependent," or "trade-off."

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.
Your extraction uses tool use with a JSON schema in which property_type is defined as an enum:
house, apartment, condo, or townhouse. After deployment, 8% of extractions fail schema validation. Investigation reveals that listings mention many uncommon property types--"studio,"
"loft," "duplex," "mobile home," "tiny house," and "converted warehouse"--and new types continue appearing regularly.
What is the most effective long-term solution?

A. Continuously expand the enum to include newly observed property types and add monitoring for additional edge cases.
B. Add few-shot examples demonstrating how to map unexpected property types to the closest existing enum value.
C. Change property_type from an enum to a free-form string and implement a normalization step in post-processing.
D. Add an other value to the enum with a separate property_type_detail string field for specifics when other is selected.

Question 3

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.
Your extraction system processes two document types: standard monthly reports, which are archived after processing, and urgent exception reports, which must trigger business alerts within
30 minutes of receipt. Both use the same JSON schema. You want to minimize API costs while meeting the latency requirements.
How should you architect the processing pipeline?

A. Queue all documents and submit hourly batches, flagging urgent documents for expedited handling when batch results return.
B. Submit all documents to the Message Batches API with custom_id values for tracking. When results arrive, immediately process urgent documents and trigger delayed alerts for exceptions.
C. Route standard reports to the Message Batches API for 50% cost savings, and route urgent exception reports to the real-time Messages API.
D. Submit all documents to the real-time Messages API to ensure consistent processing latency across document types.

Question 4

You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When researching "renewable energy adoption," the web search agent returns recent statistics (2024: 35% adoption) while the document analysis agent extracts data from internal reports (2021: 18% adoption). The synthesis agent incorrectly flags these as contradictory sources rather than recognizing the data shows growth over time. What change would best enable the synthesis agent to correctly interpret such temporal differences?

A. Instruct the synthesis agent to always treat the most recent data as authoritative and place older findings in a separate historical appendix.
B. Require subagents to include publication or data collection dates in their structured outputs.
C. Configure the web search agent to only return results from the past 6 months.
D. Add a conflict resolution agent that automatically discards older data when newer data exists for the same metric.

Question 5

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.
A security audit requires updating your authentication library from v2 to v3. The migration guide documents breaking changes: authenticate() now returns a Promise instead of accepting a callback, the User type has restructured fields, and three deprecated methods were removed.
Grep shows the library is imported in 45 files across several modules.
What's the most effective approach?

A. Enter plan mode to explore library usage across modules, map affected code paths, then create a migration strategy before implementing.
B. Paste the migration guide's breaking changes into your prompt and use direct execution to update all usages across the 45 files.
C. Update the dependency version, run the test suite, and use Claude Code to fix each failure as it appears.
D. Create a custom slash command encapsulating the migration transformations, then execute it against each file without prior codebase exploration.

Solutions: