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Anthropic CCA-F Exam Syllabus Topics:

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

1. The synthesis agent receives summarized findings from the web search and document analysis agents, then passes a consolidated summary to the report generator. During testing, you discover the generated reports make factual claims without proper citations - the report generator cannot attribute statements to their original sources because that metadata was lost during the summarization steps. What's the most effective approach to ensure proper source attribution in the final reports?

A) Have the report generator query the web search agent to re-locate sources for claims in the final report.
B) Have each agent output structured data separating content summaries from source metadata (URLs, document names, page numbers).
C) Instruct the synthesis agent to embed source references inline within its summary text using a consistent citation format.
D) Skip summarization and pass full raw outputs from web search and document analysis directly to the report generator.

2. 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.
An engineer used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth - having the agent propose specific code changes for each - before deciding which to implement. What's the most effective way to structure this exploration?

A) Resume yesterday's session and explore both approaches sequentially within the same conversation thread.
B) Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.
C) Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.
D) Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.

3. The coordinator agent has AgentDefinitions configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice the coordinator correctly reasons about when to delegate - it generates messages like "I'll ask the web search agent to find sources on this topic" - but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?

A) The AgentDefinitions are configured correctly, but the coordinator's system prompt doesn't explicitly list the available subagent types, preventing the model from knowing they can be invoked.
B) The coordinator's max_tokens setting is too low, causing the Task tool invocation to be truncated before the subagent type parameter can be specified.
C) The coordinator's allowedTools configuration doesn't include "Task", so while it can reason about delegation, it cannot invoke the tool required to spawn subagents.
D) Subagent context isolation means task descriptions from the coordinator don't automatically reach subagents; you need to configure explicit context forwarding in ClaudeAgentOptions.

4. Your update_user_profile tool accepts a user_id (required) and an optional fields_to_update object. In testing, Claude frequently omits user_id or passes incorrectly structured data. What is most critical for helping Claude understand what parameter values to provide?

A) Clear parameter descriptions explaining expected format, such as "user_id: UUID of the user to update (required)"
B) Verbose parameter names encoding format hints, such as user_id_string_uuid_format
C) Strict JSON Schema type constraints marking user_id as required and defining fields_to_update as an object type
D) Detailed error responses explaining why invalid parameter values were rejected

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 new payment processing module that must follow your project's established patterns for database transactions, error handling, and audit logging. You've identified three existing modules that exemplify these patterns: db_utils.py, error_handlers.py, and audit_logger.py. This is a one-off integration task - these patterns are well-documented in your team wiki and don't need additional project-level documentation. What's the most effective approach?

A) Ask Claude to explore your codebase to find and understand the transaction, error handling, and logging patterns before generating the new module.
B) Describe the patterns from the three modules in natural language in your prompt, explaining the transaction handling approach, error format, and logging conventions Claude should follow.
C) Add documentation of each pattern to your CLAUDE.md file, establishing them as project conventions that Claude will apply automatically.
D) Use @ references to include the three modules directly in your prompt, giving Claude concrete code examples of the patterns to follow.

Solutions: