Databricks DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK Online Practice Questions and Exam Preparation

Databricks DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK Online Questions & Answers

• Question 61:

  Which of the following statements about Spark's execution hierarchy is correct?

  A. In Spark's execution hierarchy, a job may reach over multiple stage boundaries.
  B. In Spark's execution hierarchy, manifests are one layer above jobs.
  C. In Spark's execution hierarchy, a stage comprises multiple jobs.
  D. In Spark's execution hierarchy, executors are the smallest unit.
  E. In Spark's execution hierarchy, tasks are one layer above slots.
  A. In Spark's execution hierarchy, a job may reach over multiple stage boundaries.

  ---

  In Spark's execution hierarchy, a job may reach over multiple stage boundaries. Correct. A job is a sequence of stages, and thus may reach over multiple stage boundaries. In Spark's execution hierarchy, tasks are one layer above slots. Incorrect. Slots are not a part of the execution hierarchy. Tasks are the lowest layer. In Spark's execution hierarchy, a stage comprises multiple jobs. No. It is the other way around ?a job consists of one or multiple stages. In Spark's execution hierarchy, executors are the smallest unit. False. Executors are not a part of the execution hierarchy. Tasks are the smallest unit! In Spark's execution hierarchy, manifests are one layer above jobs. Wrong. Manifests are not a part of the Spark ecosystem.

• Question 62:

  Which of the following code blocks returns a one-column DataFrame of all values in column supplier of DataFrame itemsDf that do not contain the letter X? In the DataFrame, every value should only be listed once.

  Sample of DataFrame itemsDf:

  1.+------+--------------------+--------------------+-------------------+

  2.|itemId| itemName| attributes| supplier|

  3.+------+--------------------+--------------------+-------------------+

  4.| 1|Thick Coat for Wa...|[blue, winter, cozy]|Sports Company Inc.|

  5.| 2|Elegant Outdoors ...|[red, summer, fre...| YetiX|

  6.| 3| Outdoors Backpack|[green, summer, t...|Sports Company Inc.|

  7.+------+--------------------+--------------------+-------------------+

  A. itemsDf.filter(col(supplier).not_contains('X')).select(supplier).distinct()
  B. itemsDf.select(~col('supplier').contains('X')).distinct()
  C. itemsDf.filter(not(col('supplier').contains('X'))).select('supplier').unique()
  D. itemsDf.filter(~col('supplier').contains('X')).select('supplier').distinct()
  E. itemsDf.filter(!col('supplier').contains('X')).select(col('supplier')).unique()
  D. itemsDf.filter(~col('supplier').contains('X')).select('supplier').distinct()

  ---

  Output of correct code block:

  +-------------------+

  | supplier|

  +-------------------+

  |Sports Company Inc.|

  +-------------------+

  Key to managing this is understand which operator to use to do the opposite of an operation ?the ~ (not) operator. In addition, you should know that there is no unique() method.

  Static notebook | Dynamic notebook: See test 1, 55 (Databricks import instructions)

• Question 63:

  The code block shown below should add a column itemNameBetweenSeparators to DataFrame itemsDf. The column should contain arrays of maximum 4 strings. The arrays should be composed of the values in column itemsDf which are

  separated at - or whitespace characters. Choose the answer that correctly fills the blanks in the code block to accomplish this.

  Sample of DataFrame itemsDf:

  1.+------+----------------------------------+-------------------+

  2.|itemId|itemName |supplier |

  3.+------+----------------------------------+-------------------+

  4.|1 |Thick Coat for Walking in the Snow|Sports Company Inc.|

  5.|2 |Elegant Outdoors Summer Dress |YetiX |

  6.|3 |Outdoors Backpack |Sports Company Inc.|

  7.+------+----------------------------------+-------------------+

  Code block:

  itemsDf.__1__(__2__, __3__(__4__, "[\s\-]", __5__))

  A. 1. withColumn 2. "itemNameBetweenSeparators" 3. split 4. "itemName" 5. 4 (Correct)
  B. 1. withColumnRenamed 2. "itemNameBetweenSeparators" 3. split 4. "itemName" 5. 4
  C. 1. withColumnRenamed 2. "itemName" 3. split 4. "itemNameBetweenSeparators" 5. 4
  D. 1. withColumn 2. "itemNameBetweenSeparators" 3. split 4. "itemName" 5. 5
  E. 1. withColumn 2. itemNameBetweenSeparators 3. str_split 4. "itemName" 5. 5
  A. 1. withColumn 2. "itemNameBetweenSeparators" 3. split 4. "itemName" 5. 4 (Correct)

• Question 64:

  The code block displayed below contains an error. The code block should write DataFrame transactionsDf as a parquet file to location filePath after partitioning it on column storeId.

  Find the error.

  Code block:

  transactionsDf.write.partitionOn("storeId").parquet(filePath)

  A. The partitioning column as well as the file path should be passed to the write() method of DataFrame transactionsDf directly and not as appended commands as in the code block.
  B. The partitionOn method should be called before the write method.
  C. The operator should use the mode() option to configure the DataFrameWriter so that it replaces any existing files at location filePath.
  D. Column storeId should be wrapped in a col() operator.
  E. No method partitionOn() exists for the DataFrame class, partitionBy() should be used instead.
  E. No method partitionOn() exists for the DataFrame class, partitionBy() should be used instead.

• Question 65:

  Which of the following are valid execution modes?

  A. Kubernetes, Local, Client
  B. Client, Cluster, Local
  C. Server, Standalone, Client
  D. Cluster, Server, Local
  E. Standalone, Client, Cluster
  B. Client, Cluster, Local

• Question 66:

  Which of the following code blocks selects all rows from DataFrame transactionsDf in which column productId is zero or smaller or equal to 3?

  A. transactionsDf.filter(productId==3 or productId
  B. transactionsDf.filter((col("productId")==3) or (col("productId")
  C. transactionsDf.filter(col("productId")==3 | col("productId")
  D. transactionsDf.where("productId"=3).or("productId"
  E. transactionsDf.filter((col("productId")==3) | (col("productId")
  E. transactionsDf.filter((col("productId")==3) | (col("productId")

• Question 67:

  Which of the following DataFrame methods is classified as a transformation?

  A. DataFrame.count()
  B. DataFrame.show()
  C. DataFrame.select()
  D. DataFrame.foreach()
  E. DataFrame.first()
  C. DataFrame.select()

  ---

  DataFrame.select()

  Correct, DataFrame.select() is a transformation. When the command is executed, it is evaluated lazily and returns an RDD when it is triggered by an action.

  DataFrame.foreach()

  Incorrect, DataFrame.foreach() is not a transformation, but an action. The intention of foreach() is to apply code to each element of a DataFrame to update accumulator variables or write the

  elements to external storage. The process does not return an RDD - it is an action! DataFrame.first()

  Wrong. As an action, DataFrame.first() executed immediately and returns the first row of a DataFrame.

  DataFrame.count()

  Incorrect. DataFrame.count() is an action and returns the number of rows in a DataFrame.

  DataFrame.show()

  No, DataFrame.show() is an action and displays the DataFrame upon execution of the command.

• Question 68:

  Which of the following code blocks returns a DataFrame with a single column in which all items in column attributes of DataFrame itemsDf are listed that contain the letter i?

  Sample of DataFrame itemsDf:

  1.+------+----------------------------------+-----------------------------+-------------------+

  2.|itemId|itemName |attributes |supplier |

  3.+------+----------------------------------+-----------------------------+-------------------+

  4.|1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.|

  5.|2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |

  6.|3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|

  7.+------+----------------------------------+-----------------------------+-------------------+

  A. itemsDf.select(explode("attributes").alias("attributes_exploded")).filter(attributes_exploded.c ontains("i"))
  B. itemsDf.explode(attributes).alias("attributes_exploded").filter(col("attributes_exploded").con tains("i"))
  C. itemsDf.select(explode("attributes")).filter("attributes_exploded".contains("i"))
  D. itemsDf.select(explode("attributes").alias("attributes_exploded")).filter(col("attributes_explo ded").contains("i"))
  E. itemsDf.select(col("attributes").explode().alias("attributes_exploded")).filter(col("attributes_e xploded").contains("i"))
  D. itemsDf.select(explode("attributes").alias("attributes_exploded")).filter(col("attributes_explo ded").contains("i"))

• Question 69:

  Which of the following code blocks shows the structure of a DataFrame in a tree-like way, containing both column names and types?

  A. 1.print(itemsDf.columns) 2.print(itemsDf.types)
  B. itemsDf.printSchema()
  C. spark.schema(itemsDf)
  D. itemsDf.rdd.printSchema()
  E. itemsDf.print.schema()
  B. itemsDf.printSchema()

• Question 70:

  Which of the following statements about broadcast variables is correct?

  A. Broadcast variables are serialized with every single task.
  B. Broadcast variables are commonly used for tables that do not fit into memory.
  C. Broadcast variables are immutable.
  D. Broadcast variables are occasionally dynamically updated on a per-task basis.
  E. Broadcast variables are local to the worker node and not shared across the cluster.
  C. Broadcast variables are immutable.