Anthropic CCAR-F actual dump : Claude Certified Architect - Foundations

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Anthropic CCAR-F Exam Overview:

Certification Vendor:	Anthropic
Exam Name:	Claude Certified Architect – Foundations
Exam Number:	CCAR-F
Exam Duration:	120 minutes
Exam Price:	USD $125
Real Exam Qty:	60
Certificate Validity Period:	12 months
Related Certifications:	Claude Certified Architect
Available Languages:	English
Passing Score:	720/1000 (scaled score)
Exam Format:	1 correct answer per question, Scenario-based, Multiple Choice
Sample Questions:
Exam Way:	Online proctored or Pearson VUE test center.
Pre Condition:	Recommended for solution architects with approximately 6+ months of hands-on experience building production applications using Claude and the Anthropic API. Registration currently requires access through the Anthropic Partner Network; no mandatory prerequisite certification.
Official Syllabus URL:	https://anthropic-partners.skilljar.com/claude-certified-architect-foundations-certification

Anthropic CCAR-F Exam Syllabus Topics:

Common Questions About the Anthropic CCAR-F Exam

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

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, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer asks the agent to understand how the caching layer works before adding a new cache invalidation trigger. After initial Grep searches, the agent has identified that caching logic spans 15 files including decorators, middleware, and service classes (~8,000 lines total). What's the most effective next step for building understanding while managing context constraints?

A. Use Glob to find files matching common caching patterns (cache.py, caching/), prioritize the largest files by reading them first, then check smaller files for gaps.
B. Use Grep to search for "invalidate" and "expire" patterns across all files, then Read only those specific line ranges with minimal surrounding context.
C. Analyze imports and class hierarchies to identify the base cache class, Read that file to understand the interface, then trace specific invalidation implementations.
D. Use the Read tool to sequentially load all 15 files, building complete understanding across the full caching implementation.

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 system parses e-commerce product descriptions to extract specifications such as dimensions, weight, and materials into JSON. Despite having a well-defined schema, the model inconsistently extracts the materials field--sometimes returning "cotton blend," other times
"Cotton/Polyester mix," and occasionally omitting the field when material information is clearly present in the source.
What is the most effective way to improve extraction consistency?

A. Switch to a more capable model tier because inconsistent extraction indicates insufficient model capability.
B. Add few-shot examples showing two or three complete input-output pairs with standardized material-description formats.
C. Make the materials field required instead of optional in the schema to force the model to always extract a value.
D. Set the temperature to 0 to eliminate randomness and ensure deterministic outputs.

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, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth - having the agent propose specific code changes for each - before deciding which to implement. What's the most effective way to structure this exploration?

A. Resume yesterday's session and explore both approaches sequentially within the same conversation thread.
B. Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.
C. Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.
D. Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.

Question 4

A customer sends: "This is frustrating. I've explained my issue twice and nothing is being resolved. I want to talk to a real person NOW." The agent has not yet called any tools to investigate their account. What should the agent do?

A. Briefly explain what the agent can help with and offer to resolve the issue quickly, escalating only if the customer repeats their request.
B. Immediately call escalate_to_human with the conversation history.
C. First call get_customer and lookup_order to gather account context, then escalate to a human agent.
D. Acknowledge the frustration and ask one targeted question to understand the specific issue before escalating.

Question 5

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.
In production, you observe that simple fact-checking queries (e.g., "What year was the Paris Climate Agreement signed?") traverse all four subagents sequentially, consuming 40+ seconds and significant tokens per query. Complex comparative research benefits from the full pipeline.
Your query distribution is diverse and evolving as users discover new applications. What's the most effective approach to optimize for varying query complexity?

A. Implement pattern-based routing that categorizes queries by structure (single-fact vs.
comparative vs. analytical) and maps each category to a predefined subagent combination.
B. Create a fast-path for factual questions that bypasses subagents entirely, routing all other queries through the complete pipeline to ensure research thoroughness.
C. Have the coordinator analyze each query and dynamically decide which subagents to invoke based on its assessment of query requirements.
D. Train a query complexity classifier on labeled historical data to predict optimal subagent combinations, retraining periodically as query patterns evolve.

Solutions: