一番質高い Associate-Developer-Apache-Spark 試験問題集

Which of the following code blocks returns a new DataFrame in which column attributes of DataFrame itemsDf is renamed to feature0 and column supplier to feature1?

• A. 1.itemsDf.withColumnRenamed("attributes", "feature0")
  2.itemsDf.withColumnRenamed("supplier", "feature1")
• B. itemsDf.withColumnRenamed(attributes, feature0).withColumnRenamed(supplier, feature1)
• C. itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1")
• D. itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1"))
• E. itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1")

解説: (Tech4Exam メンバーにのみ表示されます)

Which of the following statements about DAGs is correct?

• A. Spark strategically hides DAGs from developers, since the high degree of automation in Spark means that developers never need to consider DAG layouts.
• B. DAG stands for "Directing Acyclic Graph".
• C. In contrast to transformations, DAGs are never lazily executed.
• D. DAGs can be decomposed into tasks that are executed in parallel.
• E. DAGs help direct how Spark executors process tasks, but are a limitation to the proper execution of a query when an executor fails.

解説: (Tech4Exam メンバーにのみ表示されます)

The code block shown below should return a DataFrame with two columns, itemId and col. In this DataFrame, for each element in column attributes of DataFrame itemDf there should be a separate row in which the column itemId contains the associated itemId from DataFrame itemsDf. The new DataFrame should only contain rows for rows in DataFrame itemsDf in which the column attributes contains the element cozy.
A sample of DataFrame itemsDf is below.
Code block:
itemsDf.__1__(__2__).__3__(__4__, __5__(__6__))

• A. 1. filter
  2. array_contains("cozy")
  3. select
  4. "itemId"
  5. explode
  6. "attributes"
• B. 1. filter
  2. "array_contains(attributes, cozy)"
  3. select
  4. "itemId"
  5. explode
  6. "attributes"
• C. 1. filter
  2. "array_contains(attributes, 'cozy')"
  3. select
  4. "itemId"
  5. map
  6. "attributes"
• D. 1. filter
  2. "array_contains(attributes, 'cozy')"
  3. select
  4. "itemId"
  5. explode
  6. "attributes"
• E. 1. where
  2. "array_contains(attributes, 'cozy')"
  3. select
  4. itemId
  5. explode
  6. attributes

解説: (Tech4Exam メンバーにのみ表示されます)

The code block shown below should return a DataFrame with columns transactionsId, predError, value, and f from DataFrame transactionsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__)

• A. 1. where
  2. col("transactionId"), col("predError"), col("value"), col("f")
• B. 1. select
  2. "transactionId, predError, value, f"
• C. 1. select
  2. ["transactionId", "predError", "value", "f"]
• D. 1. select
  2. col(["transactionId", "predError", "value", "f"])
• E. 1. filter
  2. "transactionId", "predError", "value", "f"

解説: (Tech4Exam メンバーにのみ表示されます)

The code block displayed below contains an error. The code block should display the schema of DataFrame transactionsDf. Find the error.
Code block:
transactionsDf.rdd.printSchema

• A. printSchema is a not a method of transactionsDf.rdd. Instead, the schema should be printed via transactionsDf.print_schema().
• B. printSchema is a method and should be written as printSchema(). It is also not callable through transactionsDf.rdd, but should be called directly from transactionsDf.
  (Correct)
• C. printSchema is only accessible through the spark session, so the code block should be rewritten as spark.printSchema(transactionsDf).
• D. There is no way to print a schema directly in Spark, since the schema can be printed easily through using print(transactionsDf.columns), so that should be used instead.
• E. The code block should be wrapped into a print() operation.

解説: (Tech4Exam メンバーにのみ表示されます)