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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

Your content curation agent discovers articles, analyzes each for relevance, then adds selected articles to themed collections. With separate discover_articles(topic), analyze_article(id), and add_to_collection(article_id, collection_id) tools, you observe 18+ sequential tool calls per request, causing latency issues. The agent must make editorial judgments about which articles fit a collection's theme - this requires seeing all candidates with their analysis scores simultaneously to select a cohesive set. What tool composition best addresses efficiency while preserving editorial judgment?

A. Create a discover_and_analyze(topic) composite tool that returns all candidates with their analysis scores, keeping add_to_collection separate for selective calls.
B. Keep all tools separate but implement response caching for analyze_article calls.
C. Add a preview_curation(topic, collection_id) tool that shows what would be added based on predefined rules, with an approve_curation() tool to confirm.
D. Create a curate_collection(topic, collection_id) tool that handles discovery, analysis, and selection internally using configurable quality thresholds.

Question 2

An enterprise wants Claude responses to remain professional across every interaction. Where should tone instructions primarily reside?

A. User prompt only
B. API key description
C. Retrieved documents
D. System prompt

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, and Glob--and integrates with Model Context Protocol (MCP) servers.
Your productivity agent connects to three MCP servers: an issue tracker with search_issues, get_issue, and create_comment; a documentation wiki with search_docs, get_page, and list_spaces; and a database explorer with run_query, get_schema, and list_databases. For cross- system questions such as, "Which database tables are affected by the authentication refactor in PROJ-1234?", the agent makes eight to ten sequential exploratory calls, lacks visibility into each server's available content, and exhausts context before completing complex investigations.
What architectural change best leverages MCP capabilities to address these problems?

A. Consolidate all three systems into one unified MCP server with cross-referencing capabilities.
B. Add an orchestrator that routes each question to one server based on keywords.
C. Add a prepare_investigation tool to each server that accepts a natural-language question and returns relevant summaries.
D. Expose each server's content catalog as MCP resources, including issue summaries, documentation hierarchies, and database schemas.

Question 4

Your pipeline reviews approximately 200 database-migration scripts daily using the Message Batches API. Each request includes a shared 8,000-token system prompt containing migration- review guidelines and schema documentation, followed by an individual migration script. You added cache_control breakpoints to the shared system prompt in every request, but monitoring shows cache-hit rates of only 32%, with misses concentrated among requests processed later in the batch window. Which change addresses the root cause without adding sequential-processing latency?

A. Configure the cache breakpoints to use the extended one-hour TTL instead of the default five- minute TTL.
B. Split the 200 requests into ten sequential batches of 20, submitting each batch only after the previous batch completes.
C. Move the cache_control breakpoint from the shared system prompt to each migration script so similar code patterns can be reused.
D. Add cache-prewarming requests with max_tokens: 0 at the beginning of every batch.

Question 5

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your schema includes a skills: string[] field. Production monitoring reveals three consistency issues:
(1) compound phrases like "Python and SQL" are sometimes kept as one
entry, sometimes split;
(2) implied but unstated skills occasionally appear in extractions;
(3) similar documents produce wildly different array lengths (5-10 vs
40+ entries).
Your prompt currently says "Extract all skills mentioned."
What's the most effective improvement?

A. Add post-extraction normalization that maps skills to a canonical taxonomy and deduplicates similar entries.
B. Add constraints: "Extract 10-20 skills maximum, one skill per entry, only explicitly named skills."
C. Enrich the schema to {skill: string, confidence: float, source_quote: string}[] to capture extraction metadata.
D. Add few-shot examples demonstrating compound phrase handling, explicit mention criteria, and appropriate entry granularity.

Solutions: