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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
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
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
Prompt Engineering & Structured Output	20%	- Structured data generation 1. Output validation and reliability 2. JSON schema enforcement - Advanced prompting techniques 1. Few-shot and chain-of-thought prompting 2. System prompts and role framing
Agentic Architecture & Orchestration	27%	- Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management - Agent design patterns 1. Agent loops and control flow 2. Hub-and-spoke multi-agent systems
Claude Code Configuration & Workflows	20%	- CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution - Configuration files and structure 1. Custom commands and workflows 2. CLAUDE.md and rules system

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q83-Q88):

NEW QUESTION # 83
You want to expose a static content catalog, such as a large database schema or internal API specification, to an MCP client without requiring Claude to make exploratory tool calls. Which MCP feature is specifically designed for this?

A. MCP Resources
B. MCP Hooks
C. MCP Prompts
D. MCP Subagents

Answer: A

Explanation:
MCP Resources are used to expose static or templated data catalogs (like documentation, static data, or schemas) that clients can fetch directly to inject into prompts, saving context and eliminating the need for exploratory tool actions.

NEW QUESTION # 84
When implementing a self-evaluation loop for structured data extraction, your agent reports 96% aggregate accuracy, but downstream users complain about frequent errors on complex contracts. What monitoring methodology resolves this evaluation visibility gap?

A. Increasing the overall sampling size randomly to capture a broader dataset.
B. Tracking accuracy by specific document type and field segment (stratified metrics).
C. Switching entirely to the Message Batches API for asynchronous processing.
D. Executing the evaluation passes inside a PreToolUse hook.

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 before automating high-confidence extractions.

NEW QUESTION # 85
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.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?

A. Have the agent maintain a scratchpad file that records key findings, referencing it for subsequent questions.
B. Create summaries of all source files before exploration begins, loading only these compressed representations into context.
C. Switch to a higher-capacity model tier to provide more context window space for accumulated exploration data.
D. Implement automatic context clearing every 15 minutes to ensure the agent starts with fresh, uncontaminated context.

Answer: A

Explanation:
Long exploration sessions fill the context window and degrade recall. A persistent scratchpad externalizes important architectural findings so the agent can re-read them instead of relying solely on increasingly crowded conversation context.

NEW QUESTION # 86
A application features a tool designed to fetch a user's profile. During execution, the tool fails to connect to the database due to a timeout. Why is silently returning an empty array [ ] in this scenario considered a catastrophic anti-pattern?

A. It causes the agent to misinterpret an access failure (could not check) as a valid empty result, wrongly concluding the user does not exist.
B. It forces the model to ignore all subsequent tool calls in the session.
C. It causes the Claude API to throw a 400 Bad Request error due to missing required schema fields.
D. It prevents the use of few-shot prompting in the next iteration.

Answer: A

Explanation:
Access failures (e.g., database down, timeout) must be explicitly distinguished from empty results (e.g., checked database, found zero matches). Silently returning an empty array for an access failure misleads the agent into believing the data does not exist, leading to completely incorrect assumptions.

NEW QUESTION # 87
Your document extraction tool uses ML models to extract invoice fields (vendor, amount, date).
The models return confidence scores (0.0-1.0) for each extracted field. In production, you observe: (1) the agent proceeds with low-confidence extractions that are incorrect 23% of the time, and (2) the agent requests unnecessary human review for 31% of extractions that were actually correct. How should you restructure the tool's output?

A. Return fields with confidence scores, plus a request_review boolean computed using your tested confidence thresholds, along with a review_reasons array explaining which fields triggered review.
B. Return fields with their raw confidence scores and add detailed few-shot examples to your system prompt demonstrating how to interpret different confidence ranges and when to request human review.
C. Compute an aggregate extraction_quality score across all fields and return it alongside the extracted values. Include a text summary describing the overall extraction reliability.
D. Return fields organized into verified and needs_verification objects based on confidence thresholds.

Answer: A

Explanation:
Including a request_review flag based on confidence thresholds, along with reasons for review, gives the agent clear guidance on when human intervention is needed. This reduces both unnecessary reviews and errors caused by low-confidence extractions, improving overall reliability and efficiency.

NEW QUESTION # 88
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