A data engineer is working on a Databricks project that utilizes cloud storage. The data engineer wants to load several json files from containers on a storage account as soon as the file arrives within the storage account.
Which syntax should the data engineer follow to first load the files into a dataframe and check that it is working as expected using Python?

• A.df = spark.readStream.format( " cloudFiles " ) .option( " cloudFiles.format " , " json " ) .load( " /input
  /path " )
• B.df = spark.readStream.format( " json " ).load( " input/path " )
• C.df = spark.readStream.format( " cloud " ),option( " json " ).load( " /input/path " )
• D.df = spark.read.json( " inp i./path " )

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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.None of these changes will need to be made
• B.The pipeline will need to stop using the medallion-based multi-hop architecture
• C.The pipeline will need to be written entirely in SQL
• 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 Python

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Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

• A.Parquet files can be partitioned
• B.CREATE TABLE AS SELECT statements cannot be used on files
• C.Parquet files have a well-defined schema
• D.Parquet files have the ability to be optimized
• E.Parquet files will become Delta tables

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A data engineer is joining a very large events_df DataFrame containing billions of rows with a small country_lookup_df DataFrame containing fewer than 10,000 rows. The DataFrames are joined on the country_code column. The join is slow because of data shuffling.
How should the data engineer improve performance?

• A.Broadcast events_df when performing the join.
• B.Broadcast country_lookup_df when performing the join.
• C.Cache both DataFrames, and then run the same join.
• D.Persist both DataFrames, and then run the same join.

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The Delta transaction log for the 'students' tables is shown using the 'DESCRIBE HISTORY students' command. A Data Engineer needs to query the table as it existed before the UPDATE operation listed in the log.

Which command should the Data Engineer use to achieve this? (Choose two.)

• A.SELECT * FROM students FROM HISTORY VERSION AS OF 3
• B.SELECT * FROM students@v4
• C.SELECT * FROM students VERSION AS OF 5
• D.SELECT * FROM students TIMESTAMP AS OF '2024-04-22T 14:32:58.000+00:00'
• E.SELECT * FROM students TIMESTAMP AS OF '2024-04-22T 14:32:47.000+00:00'

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