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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. A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

A) save with mode ErrorIfExists
B) save with mode Ignore
C) saveAsTable with mode Overwrite
D) saveAsTable with mode ErrorIfExists

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

3. A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

A) Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
B) Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
C) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
D) Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.

4. 4 of 55.
A developer is working on a Spark application that processes a large dataset using SQL queries. Despite having a large cluster, the developer notices that the job is underutilizing the available resources. Executors remain idle for most of the time, and logs reveal that the number of tasks per stage is very low. The developer suspects that this is causing suboptimal cluster performance.
Which action should the developer take to improve cluster utilization?

A) Enable dynamic resource allocation to scale resources as needed
B) Increase the size of the dataset to create more partitions
C) Reduce the value of spark.sql.shuffle.partitions
D) Increase the value of spark.sql.shuffle.partitions

5. An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A) Use the command spark-submit with the -verbose flag to print the logs to the console.
B) Locate the executor logs on the Spark master node, typically under the /tmp directory.
C) Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
D) Fetch the logs by running a Spark job with the spark-sql CLI tool.

Solutions: