Anthropic Claude Certified Architect Foundations (CCA-F) : CCA-F Exam Questions

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Anthropic CCA-F Exam Syllabus Topics:

Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:

1. Your product search tool queries an external catalog API and returns matching items. In production, you observe the agent frequently retries searches immediately after receiving zero results, treating "no matches found" as a failure requiring retry. The external API returns HTTP 200 with an empty results array - a valid response. How should you restructure the tool's result to help the agent correctly interpret empty result sets?

A) Return a result object with isError: true and a message explaining no products matched.
B) Return a natural language string describing the outcome, allowing the agent to interpret the result contextually based on the message content.
C) Return a structured result with a success boolean and results array, reserving isError: true for actual execution failures only.
D) Add a suggestions field containing alternative search strategies when results are empty, helping guide the agent toward more productive follow-up queries.

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

3. Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?

A) Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.
B) Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.
C) Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
D) Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.

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.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?

A) Implement automatic context clearing every 15 minutes to ensure the agent starts with fresh, uncontaminated context.
B) Create summaries of all source files before exploration begins, loading only these compressed representations into context.
C) Have the agent maintain a scratchpad file that records key findings, referencing it for subsequent questions.
D) Switch to a higher-capacity model tier to provide more context window space for accumulated exploration data.

5. 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 caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires. What's the most effective way to start your iterative workflow?

A) Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
B) Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.
C) Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.
D) Use plan mode to analyze the current/products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.

Solutions: