Session 2 - Module C: Test Solutions¶
Multiple Choice Test - Module C Solutions
Question 1: What benefits do Pydantic models provide for LangChain tool development?
Answer: B) Type safety, automatic validation, and documentation generation
Explanation: Pydantic models provide type safety through field definitions, automatic validation of inputs and outputs, and automatic documentation generation through field descriptions. This creates robust, self-documenting tools that prevent runtime errors and improve developer experience.
Question 2: What configuration options are set in the AdvancedToolInput Config class?
Answer: B) Extra fields forbidden, validate on assignment, use enum values
Explanation: The Config class sets extra = "forbid" (prevents unexpected fields), validate_assignment = True (validates on assignment), and use_enum_values = True (uses enum values in serialization). These settings ensure strict validation and data integrity.
Question 3: What does the @validator('filters') decorator ensure?
Answer: B) Filters contain JSON-serializable values
Explanation: The @validator('filters') decorator uses json.dumps(v) to test serialization and raises a ValueError if the filters contain non-JSON-serializable values. This prevents runtime errors when filters are processed or transmitted.
Question 4: What performance metrics are tracked in the AdvancedDataAnalysisTool?
Answer: B) Total analyses, average execution time, success rate, and cache hit rate
Explanation: The tool tracks comprehensive performance metrics including total_analyses (volume), avg_execution_time (performance), success_rate (reliability), and cache_hit_rate (efficiency). This enables performance monitoring and optimization decisions.
Question 5: What is the purpose of the args_schema attribute in the tool class?
Answer: B) Enable automatic validation and documentation generation
Explanation: The args_schema attribute specifies the Pydantic model for input validation (in this case, DataAnalysisInput). This enables LangChain to automatically validate tool inputs and generate documentation about expected parameters and their types.