PDFBraindumps Anthropic CCA-F Questions PDF

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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
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
Context Management & Responsible AI	15%	- Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles - Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization
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
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
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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q16-Q21):

NEW QUESTION # 16
Which of the following scenarios represent architecturally appropriate use cases for implementing hooks in a production Claude application? Choose 2 correct answers.

A. Ensuring Claude maintains a conversational and friendly tone with customers.
B. Intercepting tool calls to deterministically block policy-violating actions before they execute.
C. Guaranteeing that an extracted JSON object has no semantic reasoning errors.
D. Normalizing tool outputs, such as date formats or status codes, before they are appended to the context window.

Answer: B,D

Explanation:
Hooks are designed for programmatic, deterministic intervention during the tool lifecycle. Valid uses include PostToolUse hooks for normalizing data and PreToolUse hooks for blocking policy-violating actions. Hooks do not guarantee semantic accuracy and are not used for soft preferences like conversational tone, which are better handled via prompts.

NEW QUESTION # 17
When designing subagents to handle conflicting data from different sources during the final synthesis phase, which of the following are recommended practices? Choose 2 correct answers.

A. Arbitrarily select the value from the most recently updated database to resolve the conflict.
B. Preserve information provenance including the source, confidence level, and timestamp.
C. Silently average the conflicting numerical values to provide a balanced estimate.
D. Require subagents to output structured claim-source mappings.

Answer: B,D

Explanation:
When handling conflicting information, the system must maintain information provenance by requiring structured claim-source mappings (tracking source, confidence, and timestamp). This allows the system or a human reviewer to resolve conflicts based on reliability. Arbitrary selection or silently averaging data are architectural anti-patterns.

NEW QUESTION # 18
In an automated code review workflow, why is it considered a major architectural anti-pattern to have the same Claude session both generate code and subsequently review its own generated changes?

A. The reviewer retains the reasoning context and assumptions from the generation phase, creating confirmation bias.
B. Tools invoked during the first pass are permanently locked and cannot be reused in the review pass.
C. The Message Batches API does not support multi-turn tool calling within a single request.
D. It exceeds the maximum token limits for the context window during the generation phase.

Answer: A

Explanation:
Same-session self-review is an anti-pattern because the model retains the context and reasoning from the generation phase, leading to a blind spot or confirmation bias. Independent review instances with fresh contexts are much more effective at objectively catching subtle issues.

NEW QUESTION # 19
A customer submits a single request containing three distinct, complex issues (e.g., a missing item, a billing dispute, and a technical bug). How should the agentic workflow handle this multi-concern request?

A. Process the first issue identified and ignore the others to maintain workflow focus.
B. Politely request the user to submit three separate support tickets.
C. Decompose the request into distinct items, investigate each in parallel using shared context, and synthesize a unified resolution.
D. Escalate the conversation to a human immediately to avoid exceeding capability limits.

Answer: C

Explanation:
Best practices for multi-concern customer requests involve decomposing the overarching request into distinct sub-items, investigating each concern in parallel (potentially via subagents) using shared context, and finally synthesizing a unified resolution for the customer.

NEW QUESTION # 20
When executing a complex feature implementation with multiple valid architectural approaches, what is the recommended lifecycle pattern for utilizing Claude Code's operating modes?

A. Combine Plan mode for initial investigation and architecture design, then switch to Direct Execution to implement the agreed-upon plan.
B. Run Direct Execution in the background via the Message Batches API, passing the result to Plan Mode.
C. Use Plan Mode exclusively, letting it automatically write and commit the files without pausing for approval.
D. Use Direct Execution to write the code, followed by Plan Mode to review the code and write tests.

Answer: A

Explanation:
For complex features, the recommended pattern is combining Plan Mode for safe investigation and approach design, getting developer approval on the strategy, and then transitioning to Direct Execution for the actual implementation of the planned approach.

NEW QUESTION # 21
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