A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

• A.There is no way to indicate whether a table contains PII.
• B."COMMENT PII"
• C.TBLPROPERTIES PII
• D.COMMENT "Contains PII"
• E.PII

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A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

• A.The pipeline will need to stop using the medallion-based multi-hop architecture
• B.The pipeline will need to be written entirely in Python
• C.None of these changes will need to be made
• D.The pipeline will need to use a batch source in place of a streaming source
• E.The pipeline will need to be written entirely in SQL

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Which of the following can be used to simplify and unify siloed data architectures that are specialized for specific use cases?

• A.None of these
• B.Data lake
• C.Data warehouse
• D.All of these
• E.Data lakehouse

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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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A data engineering team is using Kafka to capture event data and then ingest it into Databricks.
The team wants to be able to see these historical events. Medallion architecture is already in place. The team wants to be mindful of costs. Where should this historical event data be stored?

• A.Gold
• B.Silver
• C.Bronze
• D.Raw layer

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