Associate-Developer-Apache-Spark-3.5 무료 덤프문제 온라인 액세스

37 of 55.
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses.
The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

• A.customerDF = customerDF \
  .withColumn("username", split(col("email"), "@").getItem(0)) \
  .withColumn("domain", split(col("email"), "@").getItem(1))
• B.customerDF = customerDF.withColumn("domain", col("email").split("@")[1])
• C.customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))
• D.customerDF = customerDF.select("email").alias("username", "domain")

What is the benefit of using Pandas on Spark for data transformations?
Options:

• A.It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost-efficient.
• B.It is available only with Python, thereby reducing the learning curve.
• C.It computes results immediately using eager execution, making it simple to use.
• D.It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.

An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?

• A.spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
• B.spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
• C.spark.read.orc("/file/test_data.orc").selected("col1", "col2")
• D.spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")

A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

• A.streaming_df. select (countDistinct ("Name") )
• B.streaming_df.orderBy("timestamp").limit(4)
• C.streaming_df.groupby("Id") .count ()
• D.streaming_df.filter (col("count") < 30).show()

A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?

• A.regions = dict(
  regions_df
  .select('region', 'region_id')
  .sort(desc('region_id'))
  .take(3)
  )
• B.regions = dict(
  regions_df
  .select('region_id', 'region')
  .limit(3)
  .collect()
  )
• C.regions = dict(
  regions_df
  .select('region', 'region_id')
  .sort('region_id')
  .take(3)
  )
• D.regions = dict(
  regions_df
  .select('region_id', 'region')
  .sort('region_id')
  .take(3)
  )