Question 1

Which of the following code blocks returns DataFrame transactionsDf sorted in descending order by column predError, showing missing values last?

Options :

A : transactionsDf.sort(asc_nulls_last("predError"))

B : transactionsDf.orderBy("predError").desc_nulls_last()

C : transactionsDf.sort("predError", ascending=False)

D : transactionsDf.desc_nulls_last("predError")

E : transactionsDf.orderBy("predError").asc_nulls_last()

Answer: C

Question 2

If spark is running in client mode, which of the following statement about is correct ?

Options :

A : Spark driver is attributed to the machine that has the most resources

B : Spark driver is randomly attributed to a machine in the cluster

C : The entire spark application is run on a single machine

D : Spark driver remainse on the client machine that submitted the application.

Answer: D

Question 3

We have an unmanaged table “my_table”

If we run the code block down below

spark.sql(“DROP TABLE IF EXISTS my_table”)

What will happen to data in my_table ?

Options :

A : No data will be removed but you will no longer be able to refer to this data by the table name.

B : The data will be dropped also

C : Spark will remove the table and also associated views

D : This is not a valid code block

Answer: A

Question 4

If we want to create a constant integer 1 as a new column ‘new_column’ in a dataframe df, which code block we should select ?

Options :

A : df.withColumn(new_column, lit(1))

B : df.withColumn(”new_column”, lit(“1”))

C : df.withColumnRenamed('new_column', lit(1))

D : df.withColumn(“new_column”, 1)

E : df.withColumn(“new_column”, lit(1))

Answer: A

Question 5

Which of the following is the idea behind dynamic partition pruning in Spark?

Options :

A : Dynamic partition pruning is intended to skip over the data you do not need in the results of a query.

B : Dynamic partition pruning concatenates columns of similar data types to optimize join performance.

C : Dynamic partition pruning performs wide transformations on disk instead of in memory.

D : Dynamic partition pruning reoptimizes physical plans based on data types and broadcast variables.

E : Dynamic partition pruning reoptimizes query plans based on runtime statistics collected during query execution.

Answer: A

Free Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Mock Exam – Practice Online Confidently