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Anthropic CCA-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q99-Q104):

NEW QUESTION # 99
A coordinator agent needs to delegate a broad research topic. How can the coordinator efficiently spawn multiple parallel subagents instead of executing them sequentially?

A. By defining a PostToolUse hook that duplicates the prompt to multiple endpoints.
B. By emitting multiple Task tool calls within a single response turn.
C. By invoking the parallel_execute method provided natively in the Agent SDK.
D. By using the fork_session command for each subagent in separate turns.

Answer: B

Explanation:
Parallel execution in a multi-agent system is achieved when the coordinator agent emits multiple Task tool calls in a single response turn. This allows the system to process the corresponding subagents simultaneously rather than across separate sequential turns.

NEW QUESTION # 100
Which of the following scenarios are best suited for prompt chaining (fixed sequential pipelines) rather than dynamic adaptive decomposition? Choose 2 correct answers.

A. A monolith-to-microservices architectural migration involving dozens of interacting dependencies.
B. An open-ended investigation into an undocumented legacy API where responses guide the next queries.
C. A data processing pipeline where raw text is summarized, then translated, and finally formatted into JSON.
D. A predictable, multi-aspect security review where the sequence of code analysis steps is static and predefined.

Answer: C,D

Explanation:
Prompt chaining is ideal for predictable, linear workflows with static sequences of steps, such as multi-aspect code reviews following a checklist, or deterministic data processing pipelines (summarize -> translate -> format). Open-ended investigations and complex migrations require dynamic adaptive decomposition.

NEW QUESTION # 101
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 submits two requests:
- Request A: "Rename the getUserData function to fetchUserProfile
everywhere it's used."
- Request B: "Improve error handling throughout the data processing
module - add try/catch blocks, meaningful error messages, and ensure
failures don't silently corrupt data."
For which request does specifying an explicit multi-phase workflow (such as analyze -> propose
-> implement with review) most improve outcome quality?

A. Neither request benefits significantly
B. Request B, the error handling task
C. Request A, the function rename task
D. Both requests benefit equally

Answer: B

Explanation:
Improving error handling is broad and ambiguous, requiring analysis of failure paths, design decisions, and review before implementation. A simple function rename is deterministic and can usually be executed directly.

NEW QUESTION # 102
When natural language descriptions of a complex data transformation continually produce inconsistent results from Claude, what is the most effective iterative refinement technique to correct this behavior?

A. Providing 2-3 concrete input/output examples to clarify the exact expected transformation requirements.
B. Changing the tool_choice setting to auto.
C. Increasing the max_tokens parameter to allow for a longer reasoning chain.
D. Appending "BE EXTREMELY PRECISE" to the end of the system prompt.

Answer: A

Explanation:
When prose descriptions are interpreted inconsistently, providing concrete input/output (few-shot) examples is the most effective iterative refinement technique to communicate exact expected transformations and formatting requirements.

NEW QUESTION # 103
When implementing a Model Context Protocol (MCP) server, what is the primary architectural purpose of defining an MCP Resource instead of an MCP Tool?

A. To expose executable functions that perform side-effects or mutations on external backend systems.
B. To manage environment variables and authentication secrets safely across the development team.
C. To provide reusable conversation templates consisting of predefined user and assistant messages for the agent.
D. To expose data catalogs or static content that can be fetched by the client and injected directly into prompts without requiring the agent to formulate a tool call.

Answer: D

Explanation:
MCP Resources are used to expose data (like documents, schemas, or issue summaries) that clients can read and inject directly into prompts, bypassing the need for exploratory tool calls. Tools are for taking actions, and Prompts are for reusable conversation templates.

NEW QUESTION # 104
......

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