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

1. A data engineer wants to create an external table from a JSON file located at/data/input.jsonwith the following requirements:
Create an external table namedusers
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', mergeSchema
'true')
B) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge
'true')
C) CREATE TABLE users USING json OPTIONS (path '/data/input.json')
D) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')

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

3. A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A) The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
B) The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
C) The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
D) The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame

4. 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 repartition() transformation with a lower number of partitions
B) Use the distinct() transformation to combine similar partitions
C) Use the sortBy() transformation to reorganize the data
D) Use the coalesce() transformation with a lower number of partitions

5. 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) Data will be lost during conversion
C) The operation will load all data into the driver's memory, potentially causing memory overflow
D) The operation will fail if the Pandas DataFrame exceeds 1000 rows

Solutions: