{
  "context": "This evaluation assesses the engineer's ability to use Kedro's SequentialRunner to execute a data processing pipeline. The focus is on correctly instantiating and using the SequentialRunner class, creating a proper pipeline with nodes, and managing data through the DataCatalog.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "SequentialRunner instantiation",
      "description": "Creates a SequentialRunner instance using the SequentialRunner class from kedro.runner",
      "max_score": 20
    },
    {
      "name": "Pipeline creation",
      "description": "Creates a Pipeline object using the pipeline() factory function or Pipeline class with multiple node instances",
      "max_score": 15
    },
    {
      "name": "Node creation",
      "description": "Creates Node objects using the node() factory function with proper func, inputs, and outputs parameters",
      "max_score": 15
    },
    {
      "name": "DataCatalog usage",
      "description": "Creates and uses a DataCatalog instance to manage datasets, using methods like DataCatalog.from_config() or direct instantiation with dataset definitions",
      "max_score": 15
    },
    {
      "name": "Runner execution",
      "description": "Executes the pipeline by calling the run() method on the SequentialRunner instance with the pipeline and catalog as arguments",
      "max_score": 20
    },
    {
      "name": "Dataset definitions",
      "description": "Defines datasets in the catalog using appropriate dataset classes such as MemoryDataset for intermediate data storage",
      "max_score": 10
    },
    {
      "name": "Input/output specification",
      "description": "Correctly specifies inputs and outputs for each node using string names that match dataset names in the catalog",
      "max_score": 5
    }
  ]
}