CCAR-F시험응시료 - CCAR-F인증시험덤프문제

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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Claude Code Configuration & Workflows	20%	- Claude Code 1. Code generation and automation 2. Configuration and project setup 3. Agent skills 4. Development workflows
Topic 2: Tool Design & MCP Integration	18%	- Tool integration 1. Resource and server integration 2. Tool interface design 3. Tool selection and safety 4. Model Context Protocol (MCP)
Topic 3: Agentic Architecture & Orchestration	27%	- Agentic architecture patterns 1. Workflow design 2. Agent orchestration 3. Planning and execution strategies 4. Single-agent and multi-agent architectures
Topic 4: Context Management & Reliability	15%	- Context handling 1. Memory strategies 2. Cost and performance optimization 3. Context window management 4. Reliability and evaluation
Topic 5: Prompt Engineering & Structured Output	20%	- Prompt design 1. Output validation 2. Prompt engineering techniques 3. Structured output and JSON schemas 4. Few-shot prompting

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CCAR-F인증시험 덤프문제 - CCAR-F퍼펙트 최신버전 덤프

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최신 Claude Certified Architect CCAR-F 무료샘플문제 (Q48-Q53):

질문 # 48
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Testing reveals that when source documents are missing certain specifications, the model fabricates plausible-sounding values to satisfy your schema's required fields. For example, a document mentioning only dimensions receives a fabricated "weight: 2.3 kg" in the extraction output.
What schema design change most effectively addresses this hallucination behavior?

A. Add a "confidence" field alongside each specification where the model self-reports its certainty, then filter out low-confidence extractions.
B. Add explicit instructions to the prompt stating "only extract information explicitly stated in the document; use placeholder text for missing values."
C. Implement semantic validation that verifies each extracted value appears in or can be inferred from the source document text.
D. Change fields that may not exist in source documents from required to optional, allowing the model to omit them.

정답：D

설명：
The schema is creating a structural incentive for fabrication. When a field is declared required, the output must contain a value even when the source document contains no corresponding evidence. Structured Outputs can guarantee that Claude's response conforms to a JSON Schema, but schema conformance does not establish that every generated value is factually supported. Anthropic's documentation shows that the required array determines which properties must be present; therefore, source-dependent properties that may legitimately be absent should not be included as required fields.
Option B corrects the problem at the contract level. Claude can omit the unavailable property rather than inventing content merely to produce valid JSON. A nullable representation could also be used when downstream systems require a stable key set, but forcing an unsupported non-null value is architecturally unsound.

질문 # 49
During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process _refund immediately - but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist. Which change directly addresses the root cause of the agent fabricating the order_id value?

A. Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.
B. Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.
C. Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
D. Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.

정답：A

설명：
The failure originates from an underspecified tool contract. A precise description tells Claude where the required parameter must come from and prevents it from fabricating an identifier.
Server-side validation in D is still valuable as a safeguard, but it detects the error rather than addressing the agent's decision-making. Anthropic identifies detailed tool and parameter descriptions as the most important factor in reliable tool use.

질문 # 50
A developer includes multiple unrelated tasks inside one extremely long prompt. What is the MOST likely outcome?

A. Reduced instruction clarity.
B. Improved reasoning.
C. Increased prompt clarity.
D. Faster inference.

정답：A

설명：
Combining many unrelated objectives in a single prompt increases ambiguity and makes it harder for Claude to identify priorities. Separating tasks into focused prompts generally produces more reliable and maintainable outputs.

질문 # 51
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 asks your agent to add comprehensive tests to a legacy codebase with 200 files and minimal existing test coverage. The engineer hasn't specified which modules to prioritize.
How should the agent decompose this open-ended task?

A. Start writing tests for the first module alphabetically, using test failures and imports to discover related files organically.
B. Systematically read all 200 files to create a complete function inventory before writing any tests, ensuring the testing plan accounts for every function before beginning.
C. Use Glob and Grep to map codebase structure, identify heavily-coupled modules, create a prioritized plan for high-impact areas, and revise as dependencies are discovered.
D. Create a fixed testing schedule upfront based on directory structure, allocating equal effort to each top-level directory regardless of code complexity or business importance.

정답：C

설명：
The task is open-ended because neither the critical modules nor the required testing sequence is known in advance. The agent should first use lightweight discovery tools to map the repository, locate existing tests, identify central modules, and determine which components have high fan-in, business significance, complex branching, or extensive external dependencies. It can then produce an initial risk-based testing plan and refine it as new dependency information appears.
Anthropic distinguishes predefined workflows from agents that dynamically control their processes and tool usage. Agents are appropriate when the required steps cannot be reliably hardcoded and must adapt to environmental evidence. During execution, they should obtain ground truth through tool results and use that feedback to determine subsequent actions.
Anthropic also identifies orchestrator-worker designs as suitable for complex coding and search tasks where the necessary subtasks depend on what the investigation reveals.

질문 # 52
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.
Production monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output.
How should you modify the system to run these subagents concurrently?

A. Switch both subagents to a Haiku-tier model instead of Sonnet to reduce their individual execution times.
B. Structure the coordinator to emit both Agent tool calls-formerly Task tool calls-in a single response rather than across separate conversation turns.
C. Create an asynchronous orchestration layer that starts separate coordinator-subagent pairs in parallel and aggregates their results.
D. Add instructions explaining the performance benefits of parallel execution and requesting that the coordinator invoke both subagents simultaneously.

정답：B

설명：
Option B expresses both independent delegations in the same orchestration turn, allowing the runtime to execute them concurrently and return their results together. The current Claude Agent SDK calls the subagent- spawning capability the Agent tool; Task is its former name. Anthropic's SDK subagent documentation explicitly identifies parallel analysis as a primary subagent use case. Claude tool responses can also contain multiple tool_use blocks , enabling independent calls to be handled as one parallel group rather than as serial model turns. Option A shortens each execution but does not remove the unnecessary wait between them and may reduce analysis quality. Option C provides useful behavioral guidance, but instructions alone do not establish the required response structure; the coordinator must actually emit both calls together. Option D duplicates coordinator execution, complicates state management, and creates unnecessary aggregation work.
The correct flow is parallel fan-out from one coordinator, followed by a synchronization point that validates both results before synthesis begins. Failures should be tracked independently so that only the unsuccessful branch requires retrying.

질문 # 53
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