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

2. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The conversion will automatically distribute the data across worker nodes
B) The operation will fail if the Pandas DataFrame exceeds 1000 rows
C) The operation will load all data into the driver's memory, potentially causing memory overflow
D) Data will be lost during conversion

3. What is a feature of Spark Connect?

A) Supports DataFrame, Functions, Column, SparkContext PySpark APIs
B) It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
C) It supports only PySpark applications
D) It has built-in authentication

4. A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

A) .trigger(continuous=True)
B) .trigger(availableNow=True)
C) .trigger(processingTime='1 second')
D) .trigger(continuous='1 second')

5. 5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

A) A stage contains multiple tasks, and each task contains multiple jobs.
B) A stage contains multiple jobs, and each job contains multiple tasks.
C) A job contains multiple stages, and each stage contains multiple tasks.
D) A job contains multiple tasks, and each task contains multiple stages.

Solutions: