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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. 29 of 55.
A Spark application is experiencing performance issues in client mode due to the driver being resource-constrained.
How should this issue be resolved?

A) Add more executor instances to the cluster.
B) Switch the deployment mode to local mode.
C) Switch the deployment mode to cluster mode.
D) Increase the driver memory on the client machine.

2. 13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
B) regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
C) regions_dict = dict(regions.take(3))
D) regions_dict = regions.select("region_id", "region_name").take(3)

3. Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?

A) spark.conf.set("spark.pandas.arrow.enabled", "true")
B) spark.conf.set("spark.sql.execution.arrow.enabled", "true")
C) spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
D) spark.conf.set("spark.sql.arrow.pandas.enabled", "true")

4. A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columns sensor_id, temperature, and timestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:

A)

B)

C)

D)

5. A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?

A) query = streaming_df.writeStream \
.format("console") \
.outputMode("complete") \
.start()
B) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.start()
C) query = streaming_df.writeStream \
.format("console") \
.option("checkpoint", "/path/to/checkpoint") \
.outputMode("append") \
.start()
D) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.option("checkpointLocation", "/path/to/checkpoint") \
.start()

Solutions: