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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic Architecture & Orchestration	27%	- Agent design patterns 1. Agent loops and control flow 2. Hub-and-spoke multi-agent systems - Anthropic Agent SDK usage 1. Task spawning and tool management 2. Session state and context handling
Topic 2: Context Management & Responsible AI	15%	- Safety and compliance 1. Constitutional AI principles 2. Refusal handling and risk mitigation - Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization
Topic 3: Claude Code Configuration & Workflows	20%	- CI/CD and development integration 1. Plan mode vs direct execution 2. Pipeline automation and review - Configuration files and structure 1. Custom commands and workflows 2. CLAUDE.md and rules system
Topic 4: Tool Design & MCP Integration	18%	- Tool definition and best practices 1. Error handling and validation 2. Tool descriptions and selection logic - Model Context Protocol (MCP) 1. Tools, resources, and prompts integration 2. MCP server and client setup
Topic 5: Prompt Engineering & Structured Output	20%	- Structured data generation 1. Output validation and reliability 2. JSON schema enforcement - Advanced prompting techniques 1. System prompts and role framing 2. Few-shot and chain-of-thought prompting

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q61-Q66):

NEW QUESTION # 61
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. Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.
B. Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.
C. Resume yesterday's session and explore both approaches sequentially within the same conversation thread.
D. Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.

Answer: A

Explanation:
Forking preserves the accumulated authentication-module context while giving each refactoring approach an independent session, preventing one exploration from influencing the other.
Anthropic's Agent SDK supports creating a new session ID when resuming with session forking enabled.

NEW QUESTION # 62
What is the primary architectural difference between programmatic enforcement and prompt-based guidance in a multi-step workflow?

A. Prompt-based guidance runs before tool execution, whereas programmatic enforcement only runs after tool execution.
B. Programmatic enforcement provides deterministic compliance (such as prerequisite gates), whereas prompt instructions have a non-zero failure rate.
C. Programmatic enforcement executes synchronously, while prompt guidance requires the Message Batches API.
D. Prompt-based guidance is 100% reliable, whereas programmatic enforcement relies on probabilistic LLM reasoning.

Answer: B

Explanation:
Programmatic enforcement (such as hooks and prerequisite gates) executes as code, providing deterministic, 100% reliable guarantees. Prompt-based guidance relies on the LLM's probabilistic pattem matching, carrying a non-zero failure rate where the model may ignore the instructions.

NEW QUESTION # 63
After integrating a local MCP server providing code analysis tools (analyze_dependencies, find_dead_code, calculate _complexity), you verify the server is healthy and tools appear in the tools/list response. However, you observe that the agent consistently uses Grep to search for import statements instead of calling analyze_dependencies -even when users explicitly ask about "code dependencies." Examining tool definitions reveals:
MCP: analyze_ dependencies - "Analyzes dependency graph"
Built-in: Grep - "Search file contents for a pattern using regular
expressions. Returns matching lines with line numbers and surrounding
context."
What's the most effective approach to improve the agent's selection of MCP tools?

A. Expand MCP tool descriptions to detail capabilities and outputs - e.g., "Builds dependency graph showing direct imports, transitive dependencies, and cycles."
B. Remove Grep from available tools when the MCP server is connected to eliminate functional overlap.
C. Add routing instructions to the system prompt specifying that dependency-related questions should use MCP tools rather than Grep.
D. Split analyze_dependencies into granular tools ( list_imports, resolve_transitive_deps, detect_circular_deps) so each has a focused purpose less likely to overlap with Grep.

Answer: A

Explanation:
Claude selects tools primarily from their names and descriptions. A specific description distinguishing dependency-graph analysis-including direct imports, transitive dependencies, and cycles-from simple text search makes analyze_dependencies the clearer choice.

NEW QUESTION # 64
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.
A critical bug is affecting production users. Error logs show exceptions in the OrderProcessing module with a clear stack trace pointing to a specific function. You haven't worked with this module before. What's the most effective approach?

A. Use plan mode to analyze the error in context of the module's design, enumerate potential root causes, and prioritize fixes systematically.
B. Start with direct execution to gather initial information, then switch to plan mode to design a comprehensive solution before implementing any changes.
C. Use direct execution to examine the stack trace, read the relevant code, and implement a fix once you identify the root cause.
D. Enter plan mode to explore the module's architecture and dependencies before attempting any fixes.

Answer: D

Explanation:
Because the module is unfamiliar, plan mode allows safe, read-only investigation of the stack trace, relevant code, and dependencies before changing production code. Anthropic recommends planning when working with unfamiliar code.

NEW QUESTION # 65
The agent verifies customer identity through a multi-step process before resetting passwords.
During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened. What's the most likely cause of this behavior?

A. The conversation history isn't being passed in subsequent API requests.
B. The verification tool is clearing the agent's internal state after each successful validation step.
C. Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
D. The prompt lacks instructions telling Claude to remember information across multiple exchanges.

Answer: A

Explanation:
Claude relies on the conversation history included in each API request to maintain continuity across turns. If earlier messages are not passed forward, the agent will not have access to previously provided details and may ask for information again as though the exchange never occurred.

NEW QUESTION # 66
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