{
  "context": "This criteria evaluates how well the engineer uses Deep Lake's advanced version control features to manage dataset branches, commits, tags, and merges for experimental ML workflows.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Dataset creation",
      "description": "Uses deeplake.create() or deeplake.open() to initialize a dataset with proper column definitions using appropriate types (e.g., types.Embedding() for embeddings, types.Text() for labels)",
      "max_score": 15
    },
    {
      "name": "Branch creation",
      "description": "Uses dataset.branch() method to create new branches and properly manages branch references",
      "max_score": 20
    },
    {
      "name": "Commit operations",
      "description": "Uses dataset.commit() method with descriptive messages to save changes on different branches",
      "max_score": 20
    },
    {
      "name": "Tag creation",
      "description": "Uses dataset.tag() method to create named tags for important dataset versions",
      "max_score": 15
    },
    {
      "name": "Merge operations",
      "description": "Uses dataset.merge() method to combine changes from a source branch into a target branch",
      "max_score": 20
    },
    {
      "name": "Branch/tag listing",
      "description": "Uses dataset.branches and dataset.tags properties to retrieve and display all branches and tags",
      "max_score": 10
    }
  ]
}