{
  "context": "This criteria evaluates how well an engineer uses Deep Lake's advanced storage features including storage concurrency configuration, type-specific compression settings, and storage metadata access. The focus is on correct usage of storage optimization APIs and type system configuration.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Storage concurrency configuration",
      "description": "Uses deeplake.storage.set_concurrency() or equivalent storage concurrency API to configure thread count for storage operations",
      "max_score": 25
    },
    {
      "name": "Dataset creation API",
      "description": "Uses deeplake.create() to create a dataset with properly structured schema including typed columns",
      "max_score": 15
    },
    {
      "name": "Image compression configuration",
      "description": "Uses types.Image() with compression parameter (e.g., sample_compression='jpeg') and quality settings to configure JPEG compression for the image column",
      "max_score": 20
    },
    {
      "name": "Embedding type configuration",
      "description": "Uses types.Embedding() with appropriate dtype (e.g., Float32) and dimensions parameter to configure the vector column for embeddings",
      "max_score": 20
    },
    {
      "name": "Storage metadata access",
      "description": "Accesses storage metadata through appropriate Deep Lake storage API (e.g., via dataset.storage or storage Reader/ResourceMeta) to retrieve size and other resource metadata",
      "max_score": 20
    }
  ]
}