Question 1

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

Question 2

The code block shown below should return a new DataFrame with a new column named “casted” who’s value is the long equivalent of column “a” which is a integer column also this dataframe should contain all the previously existing columns from DataFrame df. Choose the response that correclty fills in the numbered blanks within the code block to complete this task. Code block: df._1_(_2_)

Options :

A : 1. withColumn2. “casted”3. cast(a)

B : 1. withColumnRenamed2. casted3. col(“a”).cast(“long”)

C : 1. withColumn2. “casted”3. cast(col(“a”)

D : 1. withColumnRenamed2. “casted”3. col(“a”).cast(“long”)

E : 1. withColumn2. “casted”3. col(“a”).cast(“long”)

F : 1. withColumn2. casted3. col(a).cast(long)

Answer: A

Question 3

Which of the following code blocks returns a DataFrame with an added column to DataFrame transactionsDf that shows the unix epoch timestamps in column transactionDate as strings in the format

month/day/year in column transactionDateFormatted?

Excerpt of DataFrame transactionsDf:

Options :

A : transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="dd/MM/yyyy"))

B : transactionsDf.withColumnRenamed("transactionDate", "transactionDateFormatted", from_unixtime("transactionDateFormatted", format="MM/dd/yyyy"))

C : transactionsDf.apply(from_unixtime(format="MM/dd/yyyy")).asColumn("transactionDateFormatted")

D : transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="MM/dd/yyyy"))

E : transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate"))

Answer: D

Question 4

Which of the following 3 DataFrame operations are classified as a wide transformation ? Choose 3 answers:

Options :

A : orderBy()

B : drop()

C : repartition()

D : distinct()

E : filter()

F : cache()

Answer: A,C,D

Question 5

Which of the following code blocks returns a DataFrame with an added column to DataFrame transactionsDf that shows the unix epoch timestamps in column transactionDate as strings in the formatmonth/day/year in column transactionDateFormatted?Excerpt of DataFrame transactionsDf:

Options :

A : transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="dd/MM/yyyy"))

B : transactionsDf.withColumnRenamed("transactionDate", "transactionDateFormatted", from_unixtime("transactionDateFormatted", format="MM/dd/yyyy"))

C : transactionsDf.apply(from_unixtime(format="MM/dd/yyyy")).asColumn("transactionDateFormatted")

D : transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="MM/dd/yyyy"))

E : transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate"))

Answer: D

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