Free Databricks Associate-Developer-Apache-Spark-3.5 Exam Dumps Questions & Answers

What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

• A.  A job contains multiple tasks, and each task contains multiple stages.
• B.  A stage contains multiple jobs, and each job contains multiple tasks.
• C.  A job contains multiple stages, and each stage contains multiple tasks.
• D.  A stage contains multiple tasks, and each task contains multiple jobs.

36 of 55.
What is the main advantage of partitioning the data when persisting tables?

• A.  It optimizes by reading only the relevant subset of data from fewer partitions.
• B.  It ensures that data is loaded into memory all at once for faster query execution.
• C.  It compresses the data to save disk space.
• D.  It automatically cleans up unused partitions to optimize storage.

20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?

• A.  Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
• B.  Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
• C.  cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
• D.  persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.

42 of 55.
A developer needs to write the output of a complex chain of Spark transformations to a Parquet table called events.liveLatest.
Consumers of this table query it frequently with filters on both year and month of the event_ts column (a timestamp).
The current code:
from pyspark.sql import functions as F
final = df.withColumn("event_year", F.year("event_ts")) \
.withColumn("event_month", F.month("event_ts")) \
.bucketBy(42, ["event_year", "event_month"]) \
.saveAsTable("events.liveLatest")
However, consumers report poor query performance.
Which change will enable efficient querying by year and month?

• A.  Replace .bucketBy() with .partitionBy("event_year") only
• B.  Replace .bucketBy() with .partitionBy("event_year", "event_month")
• C.  Add .sortBy() after .bucketBy()
• D.  Change the bucket count (42) to a lower number

What is the difference between df.cache() and df.persist() in Spark DataFrame?

• A.  Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
• B.  cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK) and persist() - Can be used to set different storage levels to persist the contents of the DataFrame
• C.  persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) and cache() - Can be used to set different storage levels to persist the contents of the DataFrame.
• D.  Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_SER)