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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. 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) Locate the executor logs on the Spark master node, typically under the /tmp directory.
B) Use the command spark-submit with the -verbose flag to print the logs to the console.
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.

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

3. Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp"), " ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

A) When the groupBy transformation is applied
B) When the filter transformation is applied
C) When the count action is applied
D) When the show action is applied

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

A) The operation will load all data into the driver's memory, potentially causing memory overflow
B) Data will be lost during conversion
C) The operation will fail if the Pandas DataFrame exceeds 1000 rows
D) The conversion will automatically distribute the data across worker nodes

5. An application architect has been investigating Spark Connect as a way to modernize existing Spark applications running in their organization.
Which requirement blocks the adoption of Spark Connect in this organization?

A) Debuggability: the ability to perform interactive debugging directly from the application code
B) Complete Spark API support: the ability to migrate all existing code to Spark Connect without modification, including the RDD APIs
C) Stability: isolation of application code and dependencies from each other and the Spark driver
D) Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself

Solutions: