Exam Anthropic CCA-F Preview - CCA-F Valid Study Questions

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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic Architecture & Orchestration	27%	- Agent design patterns 1. Agent loops and control flow 2. Hub-and-spoke multi-agent systems - Anthropic Agent SDK usage 1. Task spawning and tool management 2. Session state and context handling
Topic 2: 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
Topic 3: Claude Code Configuration & Workflows	20%	- Configuration files and structure 1. Custom commands and workflows 2. CLAUDE.md and rules system - CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution
Topic 4: 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
Topic 5: Context Management & Responsible AI	15%	- Context window optimization 1. Context retention and summarization 2. Token management and truncation strategies - Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q42-Q47):

NEW QUESTION # 42
After 30+ turns, your conversational assistant shows noticeably slower responses and occasionally produces less coherent outputs. Investigation reveals: (1) average conversations reach 50,000 tokens by turn 35, (2) production logs show 94% of user messages only reference the previous 3-5 exchanges, (3) the 6% of queries referencing earlier context typically ask about information the user could easily re-state. Your goal is to improve response speed and quality while maintaining good user experience. What's the most effective approach?

A. Implement a sliding window keeping only the system prompt and last 8-10 turns. When users reference earlier context, acknowledge the limitation and ask them to re-state the relevant information.
B. Build a retrieval system that stores all conversation turns and uses semantic search to pull in relevant historical context only when the current query appears to reference past information.
C. Enable prompt caching and continue sending the complete conversation history, using cached prefixes to reduce per-request costs while preserving all context.
D. Implement a summarization layer that progressively compresses older conversation turns into a running summary while keeping the most recent 5-6 turns verbatim, maintaining full historical context in condensed form.

Answer: D

Explanation:
Progressively summarizing older conversation turns condenses historical context while keeping recent interactions verbatim, reducing token load and maintaining coherence. This approach balances response speed, memory efficiency, and user experience by preserving essential context without requiring users to repeatedly provide information.

NEW QUESTION # 43
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
A user is expanding the research system beyond its single web search agent by adding specialized data sources. They add a financial API agent that returns structured JSON with revenue, margins, and growth rates; a news monitoring agent that returns prose summaries of recent developments; and a patent analysis agent that returns structured lists of technology areas. The synthesis agent combines these into executive briefings. Currently, it converts everything to bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose narrative flow. What change would most improve briefing quality?

A. Update the synthesis agent to render each content type appropriately - financial data as tables, news as prose
B. Standardize all subagent outputs to prose summaries with inline citations
C. Add a format conversion layer between subagents and synthesis that transforms all outputs to a common intermediate representation
D. Standardize all subagent outputs to JSON with fields for claim, evidence, source, and confidence

Answer: A

Explanation:
The source outputs already contain appropriate structures; the problem is the synthesis agent flattening them into one format. Content-aware rendering preserves quantitative comparability for financial data and narrative coherence for news, producing a clearer executive briefing.

NEW QUESTION # 44
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. Request B, the error handling task
B. Request A, the function rename task
C. Neither request benefits significantly
D. Both requests benefit equally

Answer: A

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 # 45
To mitigate the 'lost in the middle' effect when passing a large set of aggregated documents to Claude, where should you place the most critical instructions and key findings summaries?

A. Evenly distributed throughout the middle of the document chunks.
B. Inside a hidden HTML comment block .
C. In a separate PostToolUse hook payload.
D. At the beginning and end of the aggregated input.

Answer: D

Explanation:
Language models reliably process information at the beginning (primacy) and end (recency) of long inputs, but may omit findings from the middle sections. Therefore, position-aware ordering dictates placing the most important information at the beginning and end.

NEW QUESTION # 46
Which of the following scenarios describe valid and effective uses of Agent SDK hooks (such as PreToolUse and PostToolUse)? Choose 2 correct answers.

A. Guaranteeing that a generated JSON object contains no semantic reasoning errors.
B. Intercepting outgoing tool calls to block policy-violating actions and redirecting to human escalation workflows.
C. Controlling the total number of subagents that can be spawned simultaneously during multi-agent orchestration.
D. Intercepting tool results to normalize heterogeneous data formats (like dates or status codes) before the model processes them.

Answer: B,D

Explanation:
Hooks are designed for programmatic intervention during the tool lifecycle. Valid uses include PostToolUse hooks for normalizing data returned by tools, and PreToolUse hooks for intercepting and blocking policy-violating actions deterministically before they execute. Hooks do not guarantee semantic accuracy or inherently manage subagent concurrency limits.

NEW QUESTION # 47
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