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Free PDF 2025 Updated Databricks Associate-Developer-Apache-Spark-3.5: Practice Databricks Certified Associate Developer for Apache Spark 3.5 - Python Mock

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q64-Q69):

NEW QUESTION # 64
A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

A. DataFrame.filter()
B. DataFrame.groupBy().agg()
C. DataFrame.select()
D. DataFrame.withColumn()

Answer: B

Explanation:
Operations that trigger data movement across partitions (like groupBy, join, repartition) result in a shuffle and a new stage.
From Spark documentation:
"groupBy and aggregation cause data to be shuffled across partitions to combine rows with the same key." Option A (groupBy + agg) → causes shuffle.
Options B, C, and D (filter, withColumn, select) → transformations that do not require shuffling; they are narrow dependencies.
Final answer: A

NEW QUESTION # 65
54 of 55.
What is the benefit of Adaptive Query Execution (AQE)?

A. It allows Spark to optimize the query plan before execution but does not adapt during runtime.
B. It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
C. It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
D. It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.

Answer: D

Explanation:
Adaptive Query Execution (AQE) is a Spark SQL feature introduced to dynamically optimize queries at runtime based on actual data statistics collected during execution.
Key benefits include:
Runtime plan adaptation: Spark adjusts the physical plan after some stages complete.
Skew handling: Automatically splits skewed partitions to balance work distribution.
Join strategy optimization: Dynamically switches between shuffle join and broadcast join depending on partition sizes.
Coalescing shuffle partitions: Reduces the number of small tasks for better performance.
Example configuration:
spark.conf.set("spark.sql.adaptive.enabled", True)
This enables AQE globally in Spark 3.5.
Why the other options are incorrect:
A: AQE adapts during runtime, not only before execution.
B: Task distribution is a base Spark feature, not specific to AQE.
C: AQE specifically addresses runtime skew and join adjustments.
Reference:
Spark SQL Adaptive Query Execution Guide - Runtime optimization, skew handling, and join strategy adjustment.
Databricks Exam Guide (June 2025): Section "Troubleshooting and Tuning Apache Spark DataFrame API Applications" - Adaptive Query Execution benefits and configuration.

NEW QUESTION # 66
Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A. Recreate the users_vw and query the data using Spark
B. Query the users_vw using Spark
C. Persist the users_vw data as a table
D. Save the users_vw definition and query using Spark

Answer: C

Explanation:
Temp views likecreateOrReplaceTempVieware session-scoped.
They disappear once the Spark session ends.
To retain data across sessions, it must be persisted:
df.write.saveAsTable("users_vw")
Thus, the view needs to be persisted as a table to survive session termination.
Reference:Databricks - Temp vs Global vs Permanent Views

NEW QUESTION # 67
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. 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.
C. Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_SER)
D. 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

Answer: D

Explanation:
df.cache() is shorthand for df.persist(StorageLevel.MEMORY_AND_DISK)
df.persist() allows specifying any storage level such as MEMORY_ONLY, DISK_ONLY, MEMORY_AND_DISK_SER, etc.
By default, persist() uses MEMORY_AND_DISK, unless specified otherwise.

NEW QUESTION # 68
Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A. Recreate the users_vw and query the data using Spark
B. Query the users_vw using Spark
C. Persist the users_vw data as a table
D. Save the users_vw definition and query using Spark

Answer: C

Explanation:
Temp views like createOrReplaceTempView are session-scoped.
They disappear once the Spark session ends.
To retain data across sessions, it must be persisted:
df.write.saveAsTable("users_vw")
Thus, the view needs to be persisted as a table to survive session termination.

NEW QUESTION # 69
......

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