CCAR-F 무료 덤프문제 온라인 액세스

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.
Your extraction pipeline processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30- day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?

• A.Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step.
• B.Implement post-extraction validation using pattern matching to detect amendments and flag those extractions for manual review.
• C.Redesign the schema so amended fields capture multiple values, each with source location and effective date.
• D.Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms.

Your automated review generates many findings per pull request, but developer feedback shows that roughly half are dismissed as "not worth addressing." Analysis reveals that dismissed findings are often technically accurate but involve minor style preferences or patterns that are acceptable in your codebase. Before adding infrastructure complexity, what prompt-design change could most effectively reduce dismissals while maintaining the detection of genuine issues?

• A.Implement a secondary classification model that filters Claude's findings according to predicted developer acceptance.
• B.Append instructions telling Claude to "only report findings you are highly confident are genuine problems."
• C.Add explicit criteria defining which issues to report, such as bugs and security defects, and which issues to skip, such as minor style preferences and accepted local patterns.
• D.Ask Claude to rate every finding's confidence from 1 to 10 and include only findings rated 8 or higher.

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 the agent to find all callers of a function before removing it. The function is defined in a core library but is also exposed through wrapper modules that rename the function for domain- specific use (e.g., calculateTax in the library becomes computeOrderTax in the orders module).
What exploration strategy will most reliably identify all callers?

• A.Search for the function name in project documentation to understand intended usage patterns and navigate to documented integration points.
• B.Use Grep to search for the function's original name across the codebase.
• C.Use Grep to find all files that import from the library or wrapper modules, then read each file to check whether it uses the function.
• D.Read the library and wrapper modules to identify all exposed names for the function, then Grep for each name across the codebase.

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.
Your schema includes a skills: string[] field. Production monitoring reveals three consistency issues:
(1) compound phrases like "Python and SQL" are sometimes kept as one
entry, sometimes split;
(2) implied but unstated skills occasionally appear in extractions;
(3) similar documents produce wildly different array lengths (5-10 vs
40+ entries).
Your prompt currently says "Extract all skills mentioned."
What's the most effective improvement?

• A.Enrich the schema to {skill: string, confidence: float, source_quote: string}[] to capture extraction metadata.
• B.Add post-extraction normalization that maps skills to a canonical taxonomy and deduplicates similar entries.
• C.Add few-shot examples demonstrating compound phrase handling, explicit mention criteria, and appropriate entry granularity.
• D.Add constraints: "Extract 10-20 skills maximum, one skill per entry, only explicitly named skills."

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?

• A.Implement a retry loop that catches JSON parsing errors and re-prompts Claude with the error details, asking it to correct the malformed output.
• B.Add explicit formatting instructions to the prompt with JSON examples, emphasizing that Claude must return only valid JSON with no surrounding text.
• C.Define a tool with a JSON schema specifying the expected structure, using tool use to constrain Claude's output to schema-compliant JSON.
• D.Use regular expressions to locate and extract JSON from the response text, handling cases where Claude includes explanatory text around the JSON block.