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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

Your automated review CI jobs take 18 seconds to initialize before Claude begins analyzing code.
Profiling reveals that the delay comes from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUDE.md files throughout your monorepo. You need to reduce startup time while ensuring that reviews still enforce your team's coding standards, which are documented in the root-level CLAUDE.md file. What is the most effective approach?

A. Replace the default prompt entirely by using --system-prompt-file ./CLAUDE.md, which bypasses default prompt assembly and loads only your project rules.
B. Run in --bare mode and pass --append-system-prompt-file ./CLAUDE.md to explicitly load your project standards while skipping all automatic discovery.
C. Keep the default initialization and add --exclude-dynamic-system-prompt-sections to reduce per- machine prompt variability and improve prompt-cache hit rates across runners.
D. Run in --bare mode and specify all review criteria directly in the -p prompt argument for every CI invocation, without referencing external files.

Question 2

Your CI pipeline performs security-focused code reviews on approximately 50 pull requests daily, currently costing $150 per day through the synchronous API. Reviews are non-blocking-- developers merge after tests pass and address findings in follow-up commits. You are evaluating the Message Batches API because it offers a 50% cost reduction. What factor most determines whether batch processing is appropriate for this use case?

A. Whether reducing per-review latency from 30?0 seconds to near-instantaneous delivery matters to your workflow.
B. Whether your result-processing system can handle reviews arriving in a different order from the order in which they were submitted.
C. Whether each review can be structured as a single request without multi-turn refinement.
D. Whether review feedback arriving up to 24 hours after pull-request creation remains actionable.

Question 3

Your process_refund tool returns two types of errors: technical errors ("503 Service Unavailable",
"Connection timeout") that are transient (5% of calls), and business errors ("Order exceeds
30 day return window", "Item already refunded") that are permanent (12% of calls). Monitoring shows the agent wastes 3-4 turns retrying business errors that can never succeed. Currently, both error types return only a plain text message to Claude. What's the most effective way to reduce wasted retries while improving customer-facing response quality?

A. Return structured error responses with retriable: false for business errors and a customer-friendly explanation for Claude to use.
B. Add few-shot examples showing how to distinguish retriable from non-retriable errors by parsing error message text.
C. Add a check_refund_eligibility tool that must be called before process_refund to prevent business rule violations.
D. Implement automatic retry logic at the tool level for technical errors only, passing business errors to Claude without retries.

Question 4

You built an LLM-powered code-review tool that analyzes pull requests and returns structured findings. Each finding is a JSON object containing file_path, line_number, issue_category--such as security or style--and description. Developers can dismiss findings they consider unhelpful, and currently 35% of findings are dismissed. You want to analyze these dismissals to understand what the system is getting wrong and improve the prompts accordingly. What change to the output structure would best support this analysis?

A. Remove the issue_category field and track dismissal rates only at the individual-finding level.
B. Add a model_confidence field from 0.0 to 1.0 and filter findings below a threshold calibrated against historical dismissal rates.
C. Expand the description field with more detailed explanations of why each issue matters and how it should be fixed.
D. Add a detected_pattern field recording the specific code construct that triggered the finding, such as single-letter loop variable.

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.
After deployment, you find that 12% of extractions contain semantic errors that pass JSON Schema validation--for example, a duration such as "30 minutes" is incorrectly placed in an ingredient-quantity field. Human reviewers have the capacity to check only 20% of extractions.
Which approach most effectively allocates reviewer attention?

A. Randomly sample 20% of extractions for review, using corrections to track accuracy and identify error patterns.
B. Review all extractions from documents with formatting anomalies, such as unusual layouts or mixed content types.
C. Prioritize the review of all extractions where required fields are empty or explicitly marked as not found.
D. Have the model output field-level confidence scores, and then calibrate review thresholds using a labeled validation set.

Solutions: