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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Agentic Architecture & Orchestration	27%	- Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management - Agent design patterns 1. Hub-and-spoke multi-agent systems 2. Agent loops and control flow
Context Management & Responsible AI	15%	- Context window optimization 1. Context retention and summarization 2. Token management and truncation strategies - Safety and compliance 1. Constitutional AI principles 2. Refusal handling and risk mitigation
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%	- Structured data generation 1. JSON schema enforcement 2. Output validation and reliability - Advanced prompting techniques 1. System prompts and role framing 2. Few-shot and chain-of-thought prompting
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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q75-Q80):

NEW QUESTION # 75
To optimize a structured data extraction pipeline, you want to route only the most uncertain documents to your limited pool of human reviewers. Which method provides the most reliable calibration for this routing?

A. Routing any document that triggers a validation-retry loop more than once.
B. Having the model output field-level confidence scores, and calibrating those thresholds using a labeled validation set.
C. Relying on the model's self-reported confidence score in the conversational text output.
D. Escalating documents based solely on the aggregate accuracy metric of the entire pipeline.

Answer: B

Explanation:
For effective human-in-the-loop routing, models should output structured, field-level confidence scores that are then calibrated against labeled validation sets. Self-reported conversational confidence is unreliable, and aggregate metrics mask specific field-level or document- type failures.

NEW QUESTION # 76
How can a coordinator agent effectively execute multiple subagents in parallel to speed up a complex research task?

A. By emitting multiple Task tool calls within a single response turn.
B. By using the fork_session command for each subagent sequentially.
C. By setting the concurrency frontmatter field to a value greater than 1.
D. By invoking the parallel_execute method provided natively in the Agent SDK.

Answer: A

Explanation:
Parallel execution in a multi-agent system is achieved when the coordinator agent emits multiple Task tool calls in a single response turn, which allows the corresponding subagents to work simultaneously.

NEW QUESTION # 77
A custom subagent named security-reviewer is defined in . claude/agents/. How does this subagent gain access to a project's deployed skills?

A. Built-in explorer agents cascade all available workspace skills to newly spawned subagents.
B. Skills must be explicitly listed in the skills field of the custom subagent's markdown definition.
C. Subagents automatically inherit all skills currently loaded by the main coordinator agent.
D. The subagent must execute the /load_skills command upon initialization.

Answer: B

Explanation:
Subagents start with a clean context and do not automatically inherit skills from the parent or main conversation. To grant a custom subagent access to skills, they must be explicitly listed in the 'skills' field of the subagent's configuration frontmatter.

NEW QUESTION # 78
An automated extraction pipeline reports 96% aggregate accuracy, but downstream users complain about frequent errors on complex contracts. What monitoring methodology resolves this visibility gap?

A. Switching entirely to the Message Batches API for asynchronous processing.
B. Tracking accuracy by specific document type and field (stratified metrics).
C. Requiring a 100% human-in-the-loop review for all documents.
D. Increasing the overall sampling size randomly to capture a broader dataset.

Answer: B

Explanation:
Aggregate accuracy metrics can mask severe per-document-type failures (e.g., contracts failing frequently while simple receipts succeed nearly 100% of the time). Tracking accuracy using stratified metrics (per document type and field) reveals these hidden failures.

NEW QUESTION # 79
After executing a requested tool in an agentic loop, how must the application handle the follow-up request to ensure the model can accurately reason about the next action?

A. Pass only the new tool result to save token space in the context window.
B. Use the /compact command to summarize the tool output before returning it to the loop.
C. Pass the tool result inside an assistant message equipped with an is_error flag.
D. Append a user message containing a tool_result block with the matching tool_use_ld, and include the full prior conversation history.

Answer: D

Explanation:
Because Claude is stateless, the application must provide the entire conversation history. When returning a tool result, it must be appended as a user message containing a tool_result block that explicitly includes the specific tool_use_id matching the original request.

NEW QUESTION # 80
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