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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 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) Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself
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) Debuggability: the ability to perform interactive debugging directly from the application code

2. A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A) df.select("Name").distinct().orderBy(df["Name"])
B) df.select("Name").distinct()
C) df.select("Name").orderBy(df["Name"].asc())
D) df.select("Name").distinct().orderBy(df["Name"].desc())

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

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

5. 11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

A) spark.sql.shuffle.partitions
B) spark.executor.memory
C) spark.executor.cores
D) spark.task.maxFailures

Solutions: