Choose CCAR-F most accurate study material

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 system extracts event metadata (date, location, organizer, attendee_count) from news articles using a JSON schema with all nullable fields. During evaluation, you observe the model frequently generates plausible but incorrect values for fields not mentioned in the article--for example, outputting "500" for attendee_count when the source contains no attendance information.
What's the most effective way to reduce these false extractions?

• A. Make all schema fields required (non-nullable) with strict validation rules to ensure the model only outputs verifiable data.
• B. Add prompt instructions to return null for any field where information is not directly stated in the source.
• C. Add a post-processing step using a second LLM call to verify each extracted value exists in the source document.
• D. Upgrade to a more capable model tier with improved instruction-following to reduce hallucination tendencies.

A legal department requires Claude to answer only when sufficient evidence exists in retrieved documents. What should Claude do if evidence is insufficient?

• A. Increase creativity.
• B. Invent a likely answer.
• C. Search social media automatically.
• D. Clearly indicate insufficient information.

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. Read the library and wrapper modules to identify all exposed names for the function, then Grep for each 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. Use Grep to search for the function's original 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 JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction system parses e-commerce product descriptions to extract specifications such as dimensions, weight, and materials into JSON. Despite having a well-defined schema, the model inconsistently extracts the materials field--sometimes returning "cotton blend," other times
"Cotton/Polyester mix," and occasionally omitting the field when material information is clearly present in the source.
What is the most effective way to improve extraction consistency?

• A. Switch to a more capable model tier because inconsistent extraction indicates insufficient model capability.
• B. Make the materials field required instead of optional in the schema to force the model to always extract a value.
• C. Set the temperature to 0 to eliminate randomness and ensure deterministic outputs.
• D. Add few-shot examples showing two or three complete input-output pairs with standardized material-description formats.

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. 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.
• B. Append instructions telling Claude to "only report findings you are highly confident are genuine problems."
• C. Ask Claude to rate every finding's confidence from 1 to 10 and include only findings rated 8 or higher.
• D. Implement a secondary classification model that filters Claude's findings according to predicted developer acceptance.