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

1. A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?

A) Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
B) Set the number of partitions equal to the total number of CPUs in the cluster
C) Set the number of partitions to a fixed value, such as 200
D) Set the number of partitions equal to the number of nodes in the cluster

2. 23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)

A) It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.
B) It eliminates the need to write any code, automatically handling all data processing.
C) It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.
D) It processes data solely on disk storage, reducing the need for memory resources.
E) It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.

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

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

4. 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 stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
B) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
C) 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.
D) 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.

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

Solutions: