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

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Anthropic CCA-F Exam Syllabus Topics:

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

1. 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.
Your code review assistant needs to analyze pull requests and provide feedback on three aspects: code style compliance, potential security issues, and documentation completeness.
Each aspect requires reading files, running analysis tools, and generating a report section. The review process follows the same three-step workflow for every PR. Which task decomposition pattern is most appropriate for this workflow?

A) Prompt chaining - break the review into sequential steps where each aspect (style, security, documentation) is analyzed separately, with outputs combined in a final synthesis step.
B) Single comprehensive prompt - include all instructions in one prompt and let the model handle all three aspects simultaneously.
C) Orchestrator-workers - have a central LLM analyze each PR to dynamically determine which checks are needed, then delegate to specialized worker LLMs for each identified subtask.
D) Routing - classify each PR by type (feature, bugfix, refactor) first, then route to different review prompts optimized for that category.

2. A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API. What is the primary mechanism your application uses to determine whether to continue the loop or stop?

A) You manually set the tool_choice parameter to "none" after the final expected tool call to force Claude to stop requesting tools.
B) You check the stop_reason field in each API response - the loop continues while it equals
"tool_use" and exits when it changes to "end_turn" or another terminal value.
C) You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.
D) You check whether Claude's response contains a text content block - if text is present, the agent has produced its final answer and the loop should exit.

3. An engineering team notices that Claude occasionally provides answers beyond the company's internal policy documents. They want responses to rely only on approved documentation whenever possible. Which solution is MOST appropriate?

A) Use Retrieval-Augmented Generation (RAG)
B) Disable system prompts
C) Increase temperature to 1.0
D) Shorten every prompt

4. 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) Add few-shot examples showing how to distinguish retriable from non-retriable errors by parsing error message text.
D) Implement automatic retry logic at the tool level for technical errors only, passing business errors to Claude without retries.

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've documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What's the most efficient first diagnostic step?

A) Create path-specific rules in .claude/rules/handlers.md with YAML frontmatter scoping the error handling instructions to your API handler files.
B) Search for conflicting instructions in ~/.claude/CLauDe.md or ~/.claude/rules/ that might override your project conventions.
C) Run /memory to check which memory files are loaded and verify your CLAUDE.md is included.
D) Add a more detailed code examples to your CLAUDE.md showing the exact ApiError usage pattern for different endpoint types.

Solutions: