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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 2: Context Management & Reliability | 15% | - Context handling
|
| Topic 3: Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Topic 4: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 5: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
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NEW QUESTION # 55
Your order management system requires tools for three distinct operations: issuing refunds (requires amount and reason), canceling orders (requires reason), and requesting reshipments (requires shipping address). Each operation shares an order_id parameter but has different additional requirements. You notice during testing that with your current unified tool design, the agent frequently omits required parameters or includes irrelevant ones. What design change will most effectively improve parameter accuracy?
Answer: A
Explanation:
Separate tools with operation-specific schemas make the required parameters clear and reduce ambiguity during tool selection. This improves parameter accuracy because each tool exposes only the fields needed for that exact action, such as refund amount and reason, cancellation reason, or reshipment address.
NEW QUESTION # 56
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.
The system needs to extract candidate information (name, contact details, skills, work experience, education) from uploaded resumes. The extracted data must strictly conform to a predefined JSON schema, as missing required fields or incorrect data types will cause downstream validation failures.
What is the most reliable approach to ensure Claude's output consistently matches the schema?
Answer: B
Explanation:
A schema-defined tool provides a structured interface between Claude and the application. The tool's input_schema can declare required properties, nested work-experience and education objects, arrays of skills, and exact data types. Claude then supplies the extracted resume information as tool-call arguments, which the application can read directly from the tool_use response.
Anthropic's tool documentation specifies that a custom tool definition includes a JSON Schema object describing its expected parameters. Current strict tool use can additionally enforce that generated tool arguments match the declared schema exactly. This removes the need to locate JSON inside free-form text or depend on post-generation repair. ( https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/implement- tool-use ) Option A is fragile because regular expressions do not provide a robust parser for arbitrary nested JSON and cannot correct missing properties or incorrect types. Option B improves formatting behavior but remains probabilistic. Option C doubles latency and creates two opportunities for information loss: first during extraction and again during reformatting.
In a production design, fields that may genuinely be absent from a resume should be optional or nullable rather than forcing invented values. The tool schema should also use strict: true where supported. Among the available choices, D provides the strongest structural guarantee and the cleanest downstream integration.
Official references/topics: Tool Use Responses, Input Schemas, Strict Tool Use, Nested Structured Extraction.
NEW QUESTION # 57
Why is evaluation important after deploying a Claude application?
Answer: D
Explanation:
Continuous evaluation allows organizations to measure accuracy, safety, consistency, and user satisfaction. Regular testing identifies regressions and supports iterative improvement of prompts, retrieval pipelines, and application workflows.
NEW QUESTION # 58
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, and Glob-and integrates with Model Context Protocol (MCP) servers.
During testing, you observe that in extended exploration sessions lasting more than 30 minutes, the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they have already explored.
What is the most effective approach to address this?
Answer: C
Explanation:
Option A moves durable, high-value discoveries outside the transient conversation history. The scratchpad should record module responsibilities, important symbols, architectural relationships, file paths, unresolved questions, and decisions supported by the code. The agent can reread this compact file after context compaction or before answering a later architectural question. Anthropic's large-codebase guidance recommends saving plans and important state to files because those artifacts survive when long conversations are compacted. Its context-management guidance also warns that accumulated file contents and command output can reduce performance during extended sessions.
Option B clears valuable information on a fixed schedule regardless of whether the current task is complete.
Option C delays the problem but does not prevent irrelevant history from crowding out important details.
Option D performs expensive summarization before the agent knows which files are relevant and may remove implementation details needed later. A focused scratchpad supports just-in-time restoration: the agent keeps the active context lean while retaining an auditable architectural map that can be updated as exploration progresses.
NEW QUESTION # 59
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.
After adding an MCP server with specialized code refactoring tools (extract_function, rename_variable, inline_function), You notice the agent still uses basic text manipulation via Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy.
Examining the configuration, you find each MCP tool has a minimal description like
"extract_function: Extracts a function from code."
What's the most effective way to improve adoption of the MCP refactoring tools?
Answer: A
Explanation:
The minimal descriptions do not distinguish the specialized refactoring tools from general- purpose Write or Bash. Anthropic recommends detailed descriptions that specify what a tool does, when to use it, when not to use it, and its inputs and outputs.
NEW QUESTION # 60
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