Why data lake architecture centralizes data in Hadoop - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read data from a Hadoop data lake using Spark.

Hadoop

df = spark.read.format([1]).load("/data/lake/path")

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Acsv

Bjson

Cxml

Dparquet

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a format not optimized for big data like XML.

The parquet format is commonly used in data lakes for efficient storage and querying.

2fill in blank

medium

Complete the code to filter data for a specific year in the data lake.

Hadoop

filtered_df = df.filter(df.year == [1])

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A2020

B"2020"

C'2020'

Dyear

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes around the year number causing type mismatch.

The year column is numeric, so the filter value should be an integer without quotes.

3fill in blank

hard

Fix the error in the code to write data back to the data lake in parquet format.

Hadoop

filtered_df.write.mode([1]).format("parquet").save("/data/lake/output")

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Aappend

Badd

Cinsert

Dupdate

Attempts:

3 left

💡 Hint

Common Mistakes

Using invalid modes like 'add' or 'update' which cause errors.

The write mode 'append' allows adding data without overwriting existing files.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Hadoop

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

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' causing wrong filtering.

The dictionary comprehension maps each word to its length only if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 0.

Hadoop

result = [1]: [2] for k, v in data.items() if v [3] 0}

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Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper().

Using '<' instead of '>'.

This comprehension creates a dictionary with keys in uppercase and includes only items with values greater than zero.