{
  "context": "This criteria evaluates how well an engineer leverages Deep Lake's built-in version control capabilities to implement dataset versioning, commit tracking, and rollback functionality. The focus is entirely on proper usage of Deep Lake's version control API methods.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Dataset creation",
      "description": "Uses deeplake.create() to create a new versioned dataset at the specified path. The dataset should be properly initialized for version control operations.",
      "max_score": 15
    },
    {
      "name": "Commit implementation",
      "description": "Uses dataset.commit() method with appropriate message parameter to save dataset changes. Should correctly pass commit messages to track different versions.",
      "max_score": 25
    },
    {
      "name": "History access",
      "description": "Uses dataset.history property to retrieve commit history. Should correctly access and return commit records with their metadata (timestamps, messages).",
      "max_score": 20
    },
    {
      "name": "History metadata extraction",
      "description": "Correctly extracts commit metadata from history entries, including commit messages and timestamps. Should properly handle the structure of commit records returned by dataset.history.",
      "max_score": 15
    },
    {
      "name": "Rollback implementation",
      "description": "Uses dataset.rollback() method to revert the dataset to a previous version. Should correctly identify and rollback to the target version based on the provided version index.",
      "max_score": 25
    }
  ]
}