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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 integration patterns 2. Tool selection and routing strategies - Tool interface design 1. Clear tool descriptions and boundaries 2. Structured tool input/output schemas
Agentic Architecture & Orchestration	27%	- Multi-agent orchestration 1. Parallel task decomposition 2. Coordinator / sub-agent patterns - Agentic loops & lifecycle design 1. Tool use loop execution (request → tool_use → result → next step) 2. Stop reason handling and control flow
Prompt Engineering & Structured Output	20%	- Tool-augmented prompting 1. Validation and retry loops 2. Tool-use driven reasoning patterns - Instruction design 1. Deterministic output formatting (e.g., JSON) 2. Few-shot prompting strategies
Context Management & Reliability	15%	- System reliability 1. Escalation strategies (fail vs retry vs human) 2. Error propagation handling - Context window optimization 1. Managing long conversation degradation 2. "Lost in the middle" mitigation strategies
Claude Code Configuration & Workflows	20%	- Claude Code configuration 1. Project-level vs user-level configuration 2. CLAUDE.md hierarchy and scoping - CI/CD and workflow integration 1. Automated pipeline integration 2. Command vs plan mode usage

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q15-Q20):

NEW QUESTION # 15
Which of the following are recommended practices for configuring subagent invocation and prompting? Choose 2 correct answers.

A. Sharing the full coordinator conversation history with every subagent to maximize context visibility across the pipeline.
B. Configuring the AgentDefinition with specific descriptions, system prompts, and carefully scoped tool restrictions for each subagent type.
C. Omitting explicit background information in the Task call, relying on automatic context inheritance from the parent.
D. Designing coordinator prompts that specify high-level research goals and quality criteria rather than rigid step-by-step procedural instructions.

Answer: B,D

Explanation:
Best practices include specifying clear research goals/quality criteria to enable subagent adaptability, and fully configuring the AgentDefinition (descriptions, system prompts, tool limits). Sharing full conversation history and assuming automatic context inheritance are heavily tested anti-patterns that lead to context pollution.

NEW QUESTION # 16
When streaming responses that include tool calls in the Claude API, why might you observe buffering delays followed by bursts of text?

A. The API automatically waits for the entire tool output to be generated before streaming begins.
B. The ContentBlockDelta events are rate-limited to 10 events per second.
C. The API is performing a secondary security check on the generated tokens to prevent prompt injection.
D. The API validates JSON chunks and waits for complete top-level key-value pairs before sending them to the client.

Answer: D

Explanation:
The Anthropic API buffers partial JSON chunks to validate complete top-level key-value pairs against your tool's schema before transmitting them. This causes short delays followed by bursts. This validation can be bypassed by enabling fine-grained tool calling.

NEW QUESTION # 17
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. Enhance the MCP tool descriptions to explain when each tool is preferable to text manipulation and clarify expected inputs and outputs.
B. Accept this as expected behavior since simpler tools like sed are more predictable than specialized refactoring tools.
C. Implement a request classifier that detects refactoring intent and automatically routes those requests to the MCP server before the agent processes them.
D. Remove the Write tool from the agent's configuration for refactoring sessions so it must use the MCP tools for code modifications.

Answer: A

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 # 18
Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating. The team's goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates. Which approach achieves this guarantee?

A. Implement a pre-tool-use hook that counts tool invocations and terminates the loop with an automatic escalation once the agent reaches 80% of its max_turns limit.
B. Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.
C. Split the workflow into two sequential agent invocations - a first agent gathers information via get_customer and lookup_order, then a second agent receives that data and handles process_refund or escalate_to_human, each with separate turn budgets.
D. Add orchestration-layer code that checks the agent's outcome after each loop termination - if the loop ended without a completed resolution or escalation, programmatically call escalate_to_human with the accumulated conversation context and tool results.

Answer: D

Explanation:
A system prompt cannot guarantee compliance, and turn-count hooks may escalate prematurely.
An orchestration-level fallback runs regardless of why the agent loop stopped, ensuring every interaction ends in either resolution or human escalation.

NEW QUESTION # 19
What is the primary architectural advantage of using glob-pattern rules in a . claude/rules/ file rather than placing a CLAUDE. md file in a subdirectory?

A. Glob patterns disable Claude's semantic inference, forcing it to use exact keyword matching for all prompts.
B. Glob patterns bypass the Model Context Protocol (MCP) server requirements for fetching dynamic rules.
C. Glob patterns allow conventions to apply to files by type regardless of their directory location, which is essential for conventions that span multiple directories.
D. Glob patterns automatically apply the rules to the global system-level /etc/c1aude/CLAUDE .md configuration.

Answer: C

Explanation:
The advantage of glob-pattern rules over directory-level CLAUDE.md files is that they can apply conventions to files spread across the codebase by type regardless of directory location (e.g., test files located next to components). Subdirectory CLAUDE.md files only apply to that specific directory and its children.

NEW QUESTION # 20
......

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