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

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

A) Data will be lost during conversion
B) The conversion will automatically distribute the data across worker nodes
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

2. Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?

A) FATAL, NONE, INFO, DEBUG
B) WARN, NONE, ERROR, FATAL
C) ALL, DEBUG, FAIL, INFO
D) ERROR, WARN, TRACE, OFF

3. A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A) Optimizing the layout of Delta files on disk
B) Collecting persistent table statistics and storing them in the metastore for future use
C) Dynamically switching join strategies
D) Improving the performance of single-stage Spark jobs

4. 45 of 55.
Which feature of Spark Connect should be considered when designing an application that plans to enable remote interaction with a Spark cluster?

A) It provides a way to run Spark applications remotely in any programming language.
B) It can be used to interact with any remote cluster using the REST API.
C) It is primarily used for data ingestion into Spark from external sources.
D) It allows for remote execution of Spark jobs.

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

Solutions: