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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

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

3. 6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?

A) Driver Nodes
B) Worker Nodes
C) CPU Cores
D) Executors

4. A data analyst builds a Spark application to analyze finance data and performs the following operations: filter, select, groupBy, and coalesce.
Which operation results in a shuffle?

A) groupBy
B) filter
C) coalesce
D) select

5. A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

A) Configure the Spark UI to automatically collect maximum times
B) Use an accumulator to record the maximum time on the driver
C) Broadcast a variable to share the maximum time among workers
D) Use an RDD action like reduce() to compute the maximum time

Solutions: