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

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

1. 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 sortBy() transformation to reorganize the data
C) Use the coalesce() transformation with a lower number of partitions
D) Use the repartition() transformation with a lower number of partitions

2. Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A) [Row(name='bambi', age=None), Row(name='alladin', age=20)]
B) [Row(name='bambi'), Row(name='alladin', age=20)]
C) [Row(name='alladin', age=20)]
D) The code throws an error due to a schema mismatch.

3. An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?

A) spark.read.orc("/file/test_data.orc").selected("col1", "col2")
B) spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
C) spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
D) spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")

4. A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:

Which block of Spark code can be used to achieve this requirement?
Options:

A) filtered_df = users_raw_df.na.drop(how='all')
B) filtered_df = users_raw_df.na.drop(how='all', thresh=None)
C) filtered_df = users_raw_df.na.drop(thresh=0)
D) filtered_df = users_raw_df.na.drop(how='any')

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

Solutions: