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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample 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 generates many findings per pull request, but developer feedback shows that approximately half are dismissed as "not worth addressing." Analysis reveals that these findings are often technically accurate but involve minor style preferences or patterns that are acceptable in the project.
Before adding infrastructure complexity, what prompt-design change would most effectively reduce dismissals while maintaining detection of genuine issues?

A) Ask Claude to rate each finding's confidence from 1 to 10 and include only findings rated 8 or higher.
B) Add a secondary classification model that filters findings according to predicted developer acceptance.
C) Define explicit reporting criteria that distinguish reportable bugs and security issues from minor style preferences and accepted local patterns.
D) Add the instruction: "Only report findings you are highly confident are genuine problems."

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.
Your team's CLAUDE.md includes a rule: "Use 4-space indentation and always run Prettier formatting." Despite this, code reviews reveal that roughly 30% of files Claude Code generates use inconsistent formatting - sometimes 2-space indentation, sometimes missing trailing commas. Adding emphasis ("IMPORTANT: You MUST use Prettier formatting") reduces violations to about 15%, but doesn't eliminate them. What is the most effective way to ensure all generated code is consistently formatted?

A) Configure a PostToolUse hook with an Edit|Write matcher that automatically runs Prettier on each file Claude modifies.
B) Add a Stop hook with a prompt-based check that evaluates whether generated code follows formatting standards and prompts Claude to fix violations.
C) Split the formatting rules into path-scoped .claude/rules/files that load when Claude works on matching file types.
D) Extract the formatting rules into a dedicated skill that Claude loads automatically when generating code, with more detailed examples of correct formatting.

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 real-time Messages API to ensure consistent processing latency across document types.
C) 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.
D) Route standard reports to the Message Batches API for 50% cost savings, and route urgent exception reports to the real-time Messages API.

4. Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating. The team's goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates. Which approach achieves this guarantee?

A) Split the workflow into two sequential agent invocations - a first agent gathers information via get_customer and lookup_order, then a second agent receives that data and handles process_refund or escalate_to_human, each with separate turn budgets.
B) Implement a pre-tool-use hook that counts tool invocations and terminates the loop with an automatic escalation once the agent reaches 80% of its max_turns limit.
C) Add orchestration-layer code that checks the agent's outcome after each loop termination - if the loop ended without a completed resolution or escalation, programmatically call escalate_to_human with the accumulated conversation context and tool results.
D) Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.

5. A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

A) Implement sliding window context that retains the most recent 30 turns.
B) Summarize earlier turns into a narrative description, preserving full message history only for the active issue.
C) Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.
D) Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.

Solutions: