Databricks platform overview in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Spark session in Databricks.

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName([1]).getOrCreate()

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Aspark

BMyApp

C"MyApp"

DappName

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to put the app name in quotes.

Using a variable name without quotes.

The appName method requires a string argument to name the Spark application.

2fill in blank

medium

Complete the code to read a CSV file into a DataFrame in Databricks.

Apache Spark

df = spark.read.format([1]).option("header", "true").load("/mnt/data/sample.csv")

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A"json"

B"csv"

C"parquet"

D"text"

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong format like "json" or "parquet".

Not putting the format name in quotes.

To read a CSV file, the format must be set to "csv".

3fill in blank

hard

Fix the error in the code to display the first 5 rows of the DataFrame.

Apache Spark

df.[1](5).show()

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Alimit

Bhead

Cshow

Dtake

Attempts:

3 left

💡 Hint

Common Mistakes

Using head(5) which returns a list, not a DataFrame.

Using 'show', which returns None and causes an AttributeError when chaining .show().

The limit method limits the DataFrame to the first n rows and returns a DataFrame, so show() can be called after it.

4fill in blank

hard

Fill both blanks to create a DataFrame with only rows where the age is greater than 30.

Apache Spark

filtered_df = df.filter(df.[1] [2] 30)

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Aage

B>

C<

Dsalary

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name like 'salary'.

Using the less than operator '<' instead of '>'.

To filter rows where age is greater than 30, use df.age > 30 inside filter().

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each word to its length if the length is greater than 3.

Apache Spark

lengths = { [1]: [2] for [3] in words if len([3]) > 3 }

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Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Mapping key to the wrong value.

The comprehension uses 'word' as the variable, maps word to len(word), and iterates over words with 'word'.