Associate-Developer-Apache-Spark 무료 덤프문제 온라인 액세스

In which order should the code blocks shown below be run in order to create a table of all values in column attributes next to the respective values in column supplier in DataFrame itemsDf?
1. itemsDf.createOrReplaceView("itemsDf")
2. spark.sql("FROM itemsDf SELECT 'supplier', explode('Attributes')")
3. spark.sql("FROM itemsDf SELECT supplier, explode(attributes)")
4. itemsDf.createOrReplaceTempView("itemsDf")

• A.4, 2
• B.2
• C.1, 2
• D.1, 3
• E.4, 3

The code block shown below should return all rows of DataFrame itemsDf that have at least 3 items in column itemNameElements. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Example of DataFrame itemsDf:
1.+------+----------------------------------+-------------------+------------------------------------------+
2.|itemId|itemName |supplier |itemNameElements |
3.+------+----------------------------------+-------------------+------------------------------------------+
4.|1 |Thick Coat for Walking in the Snow|Sports Company Inc.|[Thick, Coat, for, Walking, in, the, Snow]|
5.|2 |Elegant Outdoors Summer Dress |YetiX |[Elegant, Outdoors, Summer, Dress] |
6.|3 |Outdoors Backpack |Sports Company Inc.|[Outdoors, Backpack] |
7.+------+----------------------------------+-------------------+------------------------------------------+ Code block:
itemsDf.__1__(__2__(__3__)__4__)

• A.1. select
  2. size
  3. "itemNameElements"
  4. >3
• B.1. select
  2. count
  3. col("itemNameElements")
  4. >3
• C.1. filter
  2. size
  3. "itemNameElements"
  4. >=3
  (Correct)
• D.1. select
  2. count
  3. "itemNameElements"
  4. >3
• E.1. filter
  2. count
  3. itemNameElements
  4. >=3

Which of the following code blocks returns all unique values of column storeId in DataFrame transactionsDf?

• A.transactionsDf.distinct("storeId")
• B.transactionsDf["storeId"].distinct()
• C.transactionsDf.select(col("storeId").distinct())
• D.transactionsDf.select("storeId").distinct()
  (Correct)
• E.transactionsDf.filter("storeId").distinct()

Which of the following is one of the big performance advantages that Spark has over Hadoop?

• A.Spark achieves great performance by storing data and performing computation in memory, whereas large jobs in Hadoop require a large amount of relatively slow disk I/O operations.
• B.Spark achieves great performance by storing data in the HDFS format, whereas Hadoop can only use parquet files.
• C.Spark achieves performance gains for developers by extending Hadoop's DataFrames with a user-friendly API.
• D.Spark achieves great performance by storing data in the DAG format, whereas Hadoop can only use parquet files.
• E.Spark achieves higher resiliency for queries since, different from Hadoop, it can be deployed on Kubernetes.

In which order should the code blocks shown below be run in order to return the number of records that are not empty in column value in the DataFrame resulting from an inner join of DataFrame transactionsDf and itemsDf on columns productId and itemId, respectively?
1. .filter(~isnull(col('value')))
2. .count()
3. transactionsDf.join(itemsDf, col("transactionsDf.productId")==col("itemsDf.itemId"))
4. transactionsDf.join(itemsDf, transactionsDf.productId==itemsDf.itemId, how='inner')
5. .filter(col('value').isnotnull())
6. .sum(col('value'))

• A.3, 1, 6
• B.3, 5, 2
• C.4, 1, 2
• D.4, 6
• E.3, 1, 2