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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:

Question 1

An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

A. It filters the id values before performing the join.
B. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
C. It increases the partition size for df1 and df2.
D. It ensures that the join happens only when the id values are identical.

Question 2

2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?

A. df.write.mode("append").json("path/to/file")
B. df.write.json("path/to/file")
C. df.write.mode("overwrite").json("path/to/file")
D. df.write.option("overwrite").json("path/to/file")

Question 3

26 of 55.
A data scientist at an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user.
Before further processing, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns.
The PII columns in df_user are name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A. df_user_non_pii = df_user.remove("name", "email", "birthdate")
B. df_user_non_pii = df_user.dropFields("name", "email", "birthdate")
C. df_user_non_pii = df_user.drop("name", "email", "birthdate")
D. df_user_non_pii = df_user.select("name", "email", "birthdate")

Question 4

Given this code:

.withWatermark("event_time", "10 minutes")
.groupBy(window("event_time", "15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:

A. The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
B. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
C. Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
D. Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.

Question 5

15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

A. streaming_df.filter("count < 30")
B. streaming_df.select(countDistinct("name"))
C. streaming_df.show()
D. streaming_df.count()

Solutions: