{
  "context": "This criteria evaluates how well the engineer uses Deep Lake's utility functions for generating random test data and managing cache. The focus is on proper usage of deeplake.random() for data generation and deeplake._create_global_cache() or related cache management functions.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Dataset creation",
      "description": "Uses deeplake.create() or deeplake.empty() to create a new dataset at the specified path",
      "max_score": 15
    },
    {
      "name": "Random data generation",
      "description": "Uses deeplake.random() or equivalent random data generation utilities from the deeplake package to populate the dataset with random samples",
      "max_score": 35
    },
    {
      "name": "Schema configuration",
      "description": "Properly adds columns to the dataset based on the schema dictionary using dataset.add_column() with appropriate type specifications",
      "max_score": 20
    },
    {
      "name": "Type handling",
      "description": "Correctly handles different column types (text, int, float, embedding) using appropriate deeplake types (e.g., types.Text(), types.Int32(), types.Float32(), types.Embedding())",
      "max_score": 20
    },
    {
      "name": "Cache management",
      "description": "Uses deeplake._create_global_cache() or other cache-related utilities from deeplake to implement the clear_test_cache() function",
      "max_score": 10
    }
  ]
}