Free PDF Quiz Anthropic - Fantastic CCA-F - Braindumps Claude Certified Architect Foundations (CCA-F) Downloads

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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Tool Design & MCP Integration	18%	- Tool definition and best practices 1. Tool descriptions and selection logic 2. Error handling and validation - Model Context Protocol (MCP) 1. Tools, resources, and prompts integration 2. MCP server and client setup
Prompt Engineering & Structured Output	20%	- Advanced prompting techniques 1. System prompts and role framing 2. Few-shot and chain-of-thought prompting - Structured data generation 1. JSON schema enforcement 2. Output validation and reliability
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
Context Management & Responsible AI	15%	- Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization - Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles
Agentic Architecture & Orchestration	27%	- Agent design patterns 1. Hub-and-spoke multi-agent systems 2. Agent loops and control flow - Anthropic Agent SDK usage 1. Task spawning and tool management 2. Session state and context handling

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q102-Q107):

NEW QUESTION # 102
A quicktechie.com development team wants to run a comprehensive codebase audit analyzing technical debt every weekend. Which API approach is most architecturally sound and cost-effective?

A. A single, long-running extended session using the- -resume flag.
B. Multi-agent orchestration running locally.
C. The Message Batches API.
D. Synchronous API calls executed in parallel.

Answer: C

Explanation:
The Message Batches API is ideal for non-urgent, latency-tolerant workloads like overnight or weekend technical debt audits. It processes requests asynchronously within a 24-hour window and offers a 50% cost savings over synchronous calls.

NEW QUESTION # 103
An agent at quicktechie.com needs to process diverse data and has been given access to 18 different tools. You notice the agent frequently misroutes requests and selects the wrong tool. What is the most architecturally sound fix?

A. Set tool_choice: "any" so the agent can parallelize the execution of all 18 tools simultaneously.
B. Switch to a higher-tier model with a larger context window so it can comprehend all 18 schemas perfectly.
C. Reduce the tool count to 4-5 per agent by distributing the tools across specialized subagents.
D. Combine all 18 tools into a single monolithic tool with 20+ optional parameters to simplify the list.

Answer: C

Explanation:
Giving a single agent access to too many tools (e.g., 18 instead of the optimal 4-5) degrades tool selection reliability because it increases decision complexity and ambiguity. The best practice is to group related tools and distribute them across specialized subagents (e.g., Customer Agent, Order Agent).

NEW QUESTION # 104
Using the @anthropic-ai/claude-agent-sdk, you want to programmatically narrow the set of tools available to a specific query loop to prevent the agent from making unintended modifications. Which configuration is used to restrict tools in the SDK?

A. Inject a permissions: read-only tag into the top-level AgentDefinition.
B. Pass the allowedTooIs: [ " Read , '" ' Glob"] array inside the options object of the query() function.
C. Set tool choice: "none" to force the model to only use search tools.
D. Edit the . mcp. j son file to globally disable the write tools for the entire project.

Answer: B

Explanation:
In the @anthropic-ai/claude-agent-sdk, you restrict the toolset for a specific execution loop by passing the 'allowedTools' array (e.g., ["Read", "Glob")) in the options object of the 'query()' function. This maps to the CLI's -allowedTools flag.

NEW QUESTION # 105
When using the Claude API, how must a system prompt be formatted to successfully utilize prompt caching?

A. Provide the system prompt as a standard string and set prompt_caching: true at the top level of the API request.
B. Structure the system prompt as a dictionary with a text block and include a cache_control field set to {"type": " ephemeral " }
C. Include the system prompt inside an XML block labeled .
D. Use the Message Batches API to ensure the system prompt is automatically cached across all batched requests.

Answer: B

Explanation:
To cache a system prompt, it cannot be passed as a simple string. It must be structured as a text block (a dictionary) that explicitly contains the cache_control field set to {"type": "ephemeral"}.

NEW QUESTION # 106
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.
After adding an MCP server with specialized code refactoring tools (extract_function, rename_variable, inline_function), You notice the agent still uses basic text manipulation via Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy.
Examining the configuration, you find each MCP tool has a minimal description like
"extract_function: Extracts a function from code."
What's the most effective way to improve adoption of the MCP refactoring tools?

A. Remove the Write tool from the agent's configuration for refactoring sessions so it must use the MCP tools for code modifications.
B. Implement a request classifier that detects refactoring intent and automatically routes those requests to the MCP server before the agent processes them.
C. Accept this as expected behavior since simpler tools like sed are more predictable than specialized refactoring tools.
D. Enhance the MCP tool descriptions to explain when each tool is preferable to text manipulation and clarify expected inputs and outputs.

Answer: D

Explanation:
The minimal descriptions do not distinguish the specialized refactoring tools from general- purpose Write or Bash. Anthropic recommends detailed descriptions that specify what a tool does, when to use it, when not to use it, and its inputs and outputs.

NEW QUESTION # 107
......

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