Instant Access Databricks.Associate-Developer-Apache-Spark.v2022-07-02.q60 Actual Practice Test Engine for Free (Page 11)

Question 46

The code block shown below should return a copy of DataFrame transactionsDf without columns value and productId and with an additional column associateId that has the value 5. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__, __3__).__4__(__5__, 'value')

A.1. withColumn
2. 'associateId'
3. 5
4. remove
5. 'productId'
B.1. withNewColumn
2. associateId
3. lit(5)
4. drop
5. productId
C.1. withColumn
2. 'associateId'
3. lit(5)
4. drop
5. 'productId'
D.1. withColumnRenamed
2. 'associateId'
3. 5
4. drop
5. 'productId'
E.1. withColumn
2. col(associateId)
3. lit(5)
4. drop
5. col(productId)

Question 47

Which of the following code blocks returns a copy of DataFrame transactionsDf that only includes columns transactionId, storeId, productId and f?
Sample of DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+
2.|transactionId|predError|value|storeId|productId| f|
3.+-------------+---------+-----+-------+---------+----+
4.| 1| 3| 4| 25| 1|null|
5.| 2| 6| 7| 2| 2|null|
6.| 3| 3| null| 25| 3|null|
7.+-------------+---------+-----+-------+---------+----+

A.transactionsDf.drop(col("value"), col("predError"))
B.transactionsDf.drop("predError", "value")
C.transactionsDf.drop(value, predError)
D.transactionsDf.drop(["predError", "value"])
E.transactionsDf.drop([col("predError"), col("value")])

Question 48

Which of the following statements about executors is correct?

A.Executors are launched by the driver.
B.Executors stop upon application completion by default.
C.Each node hosts a single executor.
D.Executors store data in memory only.
E.An executor can serve multiple applications.

Question 49

The code block shown below should return the number of columns in the CSV file stored at location filePath.
From the CSV file, only lines should be read that do not start with a # character. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
__1__(__2__.__3__.csv(filePath, __4__).__5__)

A.1. size
2. spark
3. read()
4. escape='#'
5. columns
B.1. DataFrame
2. spark
3. read()
4. escape='#'
5. shape[0]
C.1. len
2. pyspark
3. DataFrameReader
4. comment='#'
5. columns
D.1. size
2. pyspark
3. DataFrameReader
4. comment='#'
5. columns
E.1. len
2. spark
3. read
4. comment='#'
5. columns

Correct Answer: E

Explanation
Correct code block:
len(spark.read.csv(filePath, comment='#').columns)
This is a challenging question with difficulties in an unusual context: The boundary between DataFrame and the DataFrameReader. It is unlikely that a question of this difficulty level appears in the exam. However, solving it helps you get more comfortable with the DataFrameReader, a subject you will likely have to deal with in the exam.
Before dealing with the inner parentheses, it is easier to figure out the outer parentheses, gaps 1 and 5. Given the code block, the object in gap 5 would have to be evaluated by the object in gap 1, returning the number of columns in the read-in CSV. One answer option includes DataFrame in gap 1 and shape[0] in gap 2. Since DataFrame cannot be used to evaluate shape[0], we can discard this answer option.
Other answer options include size in gap 1. size() is not a built-in Python command, so if we use it, it would have to come from somewhere else. pyspark.sql.functions includes a size() method, but this method only returns the length of an array or map stored within a column (documentation linked below).
So, using a size() method is not an option here. This leaves us with two potentially valid answers.
We have to pick between gaps 2 and 3 being spark.read or pyspark.DataFrameReader. Looking at the documentation (linked below), the DataFrameReader is actually a child class of pyspark.sql, which means that we cannot import it using pyspark.DataFrameReader. Moreover, spark.read makes sense because on Databricks, spark references current Spark session (pyspark.sql.SparkSession) and spark.read therefore returns a DataFrameReader (also see documentation below). Finally, there is only one correct answer option remaining.
More info:
- pyspark.sql.functions.size - PySpark 3.1.2 documentation
- pyspark.sql.DataFrameReader.csv - PySpark 3.1.2 documentation
- pyspark.sql.SparkSession.read - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3

Question 50

Which of the following describes characteristics of the Spark UI?

A.Via the Spark UI, workloads can be manually distributed across executors.
B.Via the Spark UI, stage execution speed can be modified.
C.The Scheduler tab shows how jobs that are run in parallel by multiple users are distributed across the cluster.
D.There is a place in the Spark UI that shows the property spark.executor.memory.
E.Some of the tabs in the Spark UI are named Jobs, Stages, Storage, DAGs, Executors, and SQL.

Question 46

Question 47

Question 48

Question 49

Question 50

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