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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

Your pipeline runs:
PROMPT="You are a code reviewer."
PROMPT="$PROMPT Analyze the provided diff"
PROMPT="$PROMPT for bugs, security issues,"
PROMPT="$PROMPT and style violations."
claude -p \
--dangerously-skip-permissions \
--system-prompt "$PROMPT" < diff.txt
The reviews complete and return feedback, but Claude comments only on the piped diff--it never reads surrounding files in the checked-out repository to understand broader context, even when the diff modifies a function called by many other modules. Which change to the invocation will cause Claude to read related repository files while still applying your custom review instructions?

A. Keep --system-prompt and add --allowedTools "Read, Glob, Grep" because non-interactive -p mode otherwise disables filesystem tools.
B. Remove --system-prompt entirely and place the review instructions in a root-level CLAUDE.md because --system-prompt is incompatible with tool use under -p.
C. Replace --system-prompt with --append-system-prompt so the review instructions are added to Claude Code's default prompt instead of overwriting its built-in file-reading and code-navigation guidance.
D. Stop piping the diff through standard input and embed it in the prompt string so Claude Code treats the invocation as an agentic session rather than a stream-processing operation.

Question 2

You're implementing the escalation logic for when the agent should call escalate_to_human. Your team proposes four different approaches for triggering escalation. Which approach will most reliably identify cases that genuinely require human intervention?

A. Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.
B. Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.
C. Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and trigger escalation when the frustration score exceeds a configured threshold.
D. Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer's stated issue, ensuring a reasonable attempt before involving a human.

Question 3

Your send_notification tool calls third-party messaging APIs. When these services time out during delivery, you cannot determine whether the message was actually sent. Currently, the tool returns is_error: true with a generic "Notification failed" message for all timeouts. Production monitoring reveals agents automatically retry these failures, frequently causing users to receive duplicate notifications. How should you modify the error response?

A. Return is_error: true with a message encouraging retry: "Delivery service temporarily unavailable.
Please retry the notification."
B. Return is_error: true with the original message content echoed back.
C. Return is_error: true with a message communicating uncertainty: "Timeout - status unknown.
Message may have been sent. Avoid retry."
D. Return is_error: true with a structured field retry_safe: true for timeouts, distinguishing them from permanent failures that should not be retried.

Question 4

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.
You're implementing a complex graph traversal algorithm with specific performance requirements and edge cases to handle (disconnected nodes, cycles, weighted edges). You want to structure your workflow for efficient iterative refinement with Claude. What approach will most effectively enable progressive improvement across multiple iterations?

A. Provide Claude with a reference implementation from documentation, then ask it to rewrite the code to match your codebase style and add the required edge case handling, comparing outputs against the reference.
B. Have Claude extensively research the algorithm and create a detailed implementation plan using extended thinking, then implement the complete solution based on that plan.
C. Provide Claude with a detailed natural language specification of the algorithm, including all requirements and edge cases. Review each output manually and provide descriptive feedback on what behavior needs to change.
D. Write a test suite covering expected behavior, edge cases, and performance requirements before implementation. Ask Claude to write code that passes the tests, then iterate by sharing test failures with each refinement request.

Question 5

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

Solutions: