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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

A developer wants Claude to consistently answer as an experienced cybersecurity analyst regardless of the user question. Which prompt component should contain this instruction?

A. User message
B. Assistant response
C. Retrieved document
D. System prompt

Question 2

You are building a structured data-extraction system using Claude. The system extracts information from unstructured documents, validates output against JSON schemas, and integrates the results with downstream systems.
Monitoring reveals that specifications sometimes appear inconsistently within source documents.
For example, a summary section might state "Battery: 4000 mAh," while the detailed specifications table states "Battery: 4200 mAh." Your current schema contains a single battery_capacity field.
This inconsistency occurs in approximately 15% of documents, and historical analysis confirms that the detailed specifications table is accurate 90% of the time.
What is the most effective approach?

A. Add a conflict_detected Boolean field and route every affected document for manual review.
B. Change the field to an array that captures every discovered value and its source location, leaving downstream systems to apply precedence rules.
C. Reject every extraction containing conflicting values and require the source document to be corrected before processing continues.
D. Add extraction instructions specifying that values from the detailed specifications table take precedence when conflicting values exist, while retaining the single-value schema.

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. 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.
B. Route standard reports to the Message Batches API for 50% cost savings, and route urgent exception reports to the real-time Messages API.
C. Submit all documents to the real-time Messages API to ensure consistent processing latency across document types.
D. Queue all documents and submit hourly batches, flagging urgent documents for expedited handling when batch results return.

Question 4

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 Claude Code yesterday to investigate authentication flows in a legacy monolith, building up significant context over a 2-hour session. Today she wants to continue that specific investigation. She's worked on three other codebases since then and knows the session was named "auth-deep-dive".
How should she resume?

A. Use --session-id with the UUID from yesterday's session transcript file
B. Use --continue to pick up where the most recent conversation left off
C. Use --resume auth-deep-dive to load that specific session by name
D. Start fresh and re-read the same files

Question 5

Your automated review generates many findings per pull request, but developer feedback shows that roughly half are dismissed as "not worth addressing." Analysis reveals that dismissed findings are often technically accurate but involve minor style preferences or patterns that are acceptable in your codebase. Before adding infrastructure complexity, what prompt-design change could most effectively reduce dismissals while maintaining the detection of genuine issues?

A. Ask Claude to rate every finding's confidence from 1 to 10 and include only findings rated 8 or higher.
B. Add explicit criteria defining which issues to report, such as bugs and security defects, and which issues to skip, such as minor style preferences and accepted local patterns.
C. Implement a secondary classification model that filters Claude's findings according to predicted developer acceptance.
D. Append instructions telling Claude to "only report findings you are highly confident are genuine problems."

Solutions: