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| Section | Weight | Objectives |
|---|---|---|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Context Management & Reliability | 15% | - Context handling
|
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NEW QUESTION # 53
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?
Answer: C
Explanation:
Option D addresses the observed inconsistency at the model-behavior level. A schema can require that materials be a string, but it cannot teach Claude which lexical form the application considers canonical or demonstrate when a source phrase should populate the field. Anthropic identifies examples as one of the most reliable ways to steer output format, structure, and consistency, recommending several relevant and diverse examples that mirror the real task in its prompting best practices. Complete input-output pairs show both recognition and normalization:
for example, a description containing "60% cotton, 40% polyester" can consistently map to the chosen "cotton/polyester blend" representation. They can also include difficult cases where material information is embedded indirectly in prose. Temperature zero reduces sampling variability but does not repair an underspecified transformation rule. A more capable model likewise lacks the missing formatting convention. Making the field required is dangerous because documents may legitimately omit materials; it can force unsupported values and increase hallucinations. Few-shot examples therefore supply the missing decision boundary while preserving truthful absence handling. The examples should be evaluated on held-out descriptions and supplemented by deterministic post-processing if downstream systems require an exact controlled vocabulary.
NEW QUESTION # 54
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 has been running for 3 weeks and human reviewers have corrected 847 extractions.
Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or
"a splash," the model either invents specific amounts or leaves fields empty--accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?
Answer: C
Explanation:
The reviewer corrections have exposed a narrow and repeatable interpretation failure. The desired policy is clear: informal measurements are valid source values and must be preserved verbatim rather than normalized into invented quantities or treated as missing. This behavior can be communicated efficiently through targeted few-shot examples.
Anthropic recommends examples for demonstrating expected behavior and improving consistency. Examples can pair source phrases such as "a handful of spinach," "a splash of vinegar," and "a pinch of salt" with outputs that retain handful, splash, and pinch exactly.
Additional counterexamples can show that Claude must not convert these phrases into grams, millilitres, or estimated serving quantities.
NEW QUESTION # 55
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You're implementing a new payment processing module that must follow your project's established patterns for database transactions, error handling, and audit logging. You've identified three existing modules that exemplify these patterns: db_utils.py , error_handlers.py , and audit_logger.py . This is a one-off integration task-these patterns are well-documented in your team wiki and don't need additional project-level documentation.
What's the most effective approach?
Answer: D
Explanation:
Direct @ references provide Claude with the exact implementations it must imitate. Anthropic documents that referencing a file with @ includes the full file content in the conversation, and multiple files can be referenced in one message. This gives Claude immediate access to the real transaction boundaries, exception structures, audit fields, naming conventions, and helper APIs used by the project. ( https://code.claude.com/docs/en
/common-workflows )
Option B is inappropriate because the task is explicitly one-off and the conventions are already documented elsewhere. CLAUDE.md is loaded into every session and should contain concise information that broadly applies to the project. Adding detailed implementation material for a single integration would consume context unnecessarily. Anthropic recommends moving occasional procedures to skills and keeping CLAUDE.
md limited to persistent, widely applicable guidance. ( https://code.claude.com/docs/en/memory ) Option C loses precision because a natural-language summary may omit subtle but important code behavior.
Option D asks Claude to rediscover files that have already been identified, increasing exploration time and context usage.
The most effective prompt should reference all three modules, identify which pattern each demonstrates, specify the new module's required behavior, and request focused tests proving that the established conventions were followed.
Official references/topics: @ file references, rich prompt context, CLAUDE.md scope, pattern-based implementation.
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.
Your extraction system implements automatic retries when validation fails. On each retry, the specific validation error is appended to the prompt. This retry-with-error-feedback approach resolves most failures within 2? attempts.
For which failure pattern would additional retries be LEAST effective?
Answer: D
Explanation:
Retry-with-error-feedback is effective when the required information is available and the defect concerns representation. Options A, C, and D are correctable formatting failures: the model can flatten an object into an array, remove thousands separators and emit an integer, or convert a datetime into the required date-only format. The validation message supplies enough information to revise the output.
Option B is fundamentally different. The complete co-author list is absent from the model's input and exists only in an external document. No number of retries can recover evidence that was never supplied. Repeated attempts may instead increase the probability of fabrication.
Anthropic's hallucination guidance recommends restricting responses to available documents, allowing the model to acknowledge missing information, and withholding or retracting claims that cannot be grounded in the source.
The correct response is therefore to return an explicit missing-data state, retrieve the external document through a tool, or route the record for enrichment. Retrying without changing the information boundary cannot solve the problem.
Current Anthropic Structured Outputs can eliminate many shape-related failures by guaranteeing JSON Schema conformance, but they still cannot create unavailable source facts.
NEW QUESTION # 57
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?
Answer: B
Explanation:
Option D addresses the observed inconsistency at the model-behavior level. A schema can require that materials be a string, but it cannot teach Claude which lexical form the application considers canonical or demonstrate when a source phrase should populate the field. Anthropic identifies examples as one of the most reliable ways to steer output format, structure, and consistency, recommending several relevant and diverse examples that mirror the real task in its prompting best practices . Complete input-output pairs show both recognition and normalization: for example, a description containing "60% cotton, 40% polyester" can consistently map to the chosen "cotton/polyester blend" representation. They can also include difficult cases where material information is embedded indirectly in prose. Temperature zero reduces sampling variability but does not repair an underspecified transformation rule. A more capable model likewise lacks the missing formatting convention. Making the field required is dangerous because documents may legitimately omit materials; it can force unsupported values and increase hallucinations. Few-shot examples therefore supply the missing decision boundary while preserving truthful absence handling. The examples should be evaluated on held-out descriptions and supplemented by deterministic post-processing if downstream systems require an exact controlled vocabulary.
NEW QUESTION # 58
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