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

1. 20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?

A) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
B) cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
C) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
D) persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.

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

3. 37 of 55.
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 = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))
B) customerDF = customerDF.select("email").alias("username", "domain")
C) customerDF = customerDF \
.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
D) customerDF = customerDF.withColumn("domain", col("email").split("@")[1])

4. A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A) Optimizing the layout of Delta files on disk
B) Collecting persistent table statistics and storing them in the metastore for future use
C) Improving the performance of single-stage Spark jobs
D) Dynamically switching join strategies

5. A data engineer wants to create an external table from a JSON file located at /data/input.json with the following requirements:
Create an external table named users
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:

A) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
B) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge 'true')
C) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema 'true')
D) CREATE TABLE users USING json OPTIONS (path '/data/input.json')

Solutions: