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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)
B) regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)
C) regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
)
D) regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
)

2. A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A) Use the distinct() transformation to combine similar partitions
B) Use the coalesce() transformation with a lower number of partitions
C) Use the sortBy() transformation to reorganize the data
D) Use the repartition() transformation with a lower number of partitions

3. 10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?

A) It runs on a single node only, utilizing memory efficiently.
B) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
C) It is available only with Python, thereby reducing the learning curve.
D) It computes results immediately using eager execution.

4. A data scientist is working with a Spark DataFrame called customerDF that contains customer information. The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A) customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
B) customerDF.withColumn("username", substring_index(col("email"), "@", 1)) \
.withColumn("domain", substring_index(col("email"), "@", -1))
C) customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)
D) customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)

5. A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
B) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
C) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
D) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")

Solutions: