Question 1

The code block displayed below contains an error. The code block should read the csv file located at path

data/transactions.csv into DataFrame transactionsDf, using the first row as column header

and casting the columns in the most appropriate type. Find the error.

First 3 rows of transactions.csv:

1.transactionId;storeId;productId;name

2.1;23;12;green grass

3.2;35;31;yellow sun

4.3;23;12;green grass

Code block:

transactionsDf = spark.read.load("data/transactions.csv", sep=";", format="csv", header=True)

Options :

A : The DataFrameReader is not accessed correctly.

B : The transaction is evaluated lazily, so no file will be read.

C : Spark is unable to understand the file type.

D : The code block is unable to capture all columns.

E : The resulting DataFrame will not have the appropriate schema.

Answer: A

Question 2

Which of the following 3 DataFrame operations are NOT classified as an action? Choose 3 answers:

Options :

A : foreach()

B : cache()

C : limit()

D : printSchema()

E : show()

F : first()

Answer: B,C,D

Question 3

Which of the following code blocks efficiently converts DataFrame transactionsDf from 12 into 24 partitions?

Options :

A : transactionsDf.repartition(24, boost=True)

B : transactionsDf.repartition()

C : transactionsDf.repartition("itemId", 24)

D : transactionsDf.coalesce(24)

E : transactionsDf.repartition(24)

Answer: A

Question 4

Given that the number of partitions of dataframe df is 4 and we want to write a parquet file in a given path. Choose the correct number of files after a successful write operation.

Options :

A : 4

B : 1

C : 8

Answer: A

Question 5

The code block shown below should return a single-column DataFrame with a column named consonant_ct

that, for each row, shows the number of consonants in column itemName of DataFrame

itemsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.

DataFrame itemsDf:

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

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

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

Code block:

itemsDf.select(__1__(__2__(__3__(__4__), "a|e|i|o|u|\s", "")).__5__("consonant_ct"))

Options :

A :

1. length

2. regexp_extract

3. upper

4. col("itemName")

5. as

B :

1. size

2. regexp_replace

3. lower

4. "itemName"

5. alias

C :

1. lower

2. regexp_replace

3. length

4. "itemName"

5. alias

D :

1. length

2. regexp_replace

3. lower

4. col("itemName")

5. alias

E :

1. size

2. regexp_extract

3. lower

4. col("itemName")

5. alias

Answer: D

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