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A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in...

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A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create Lakeflow Declarative Pipelines that read the raw JSON data and write it into a Delta table for further processing.

Which code snippet will correctly ingest the raw JSON data and create a Delta table using LDP?

A.

import dlt

@dlt.table

def raw_customers():

return spark.read.format( " csv " ).load( " s3://my-bucket/raw-customers/ " )

B.

import dlt

@dlt.table

def raw_customers():

return spark.read.json( " s3://my-bucket/raw-customers/ " )

C.

import dlt

@dlt.table

def raw_customers():

return spark.read.format( " parquet " ).load( " s3://my-bucket/raw-customers/ " )

D.

import dlt

@dlt.view

def raw_customers():

return spark.format.json( " s3://my-bucket/raw-customers/ " )

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