100% PASS QUIZ DATABRICKS ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 - MARVELOUS DATABRICKS CERTIFIED ASSOCIATE DEVELOPER FOR APACHE SPARK 3.5 - PYTHON INTERACTIVE QUESTIONS

100% Pass Quiz Databricks Associate-Developer-Apache-Spark-3.5 - Marvelous Databricks Certified Associate Developer for Apache Spark 3.5 - Python Interactive Questions

100% Pass Quiz Databricks Associate-Developer-Apache-Spark-3.5 - Marvelous Databricks Certified Associate Developer for Apache Spark 3.5 - Python Interactive Questions

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q57-Q62):

NEW QUESTION # 57
Given the following code snippet inmy_spark_app.py:

What is the role of the driver node?

  • A. The driver node stores the final result after computations are completed by worker nodes
  • B. The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes
  • C. The driver node holds the DataFrame data and performs all computations locally
  • D. The driver node only provides the user interface for monitoring the application

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In the Spark architecture, the driver node is responsible for orchestrating the execution of a Spark application.
It converts user-defined transformations and actions into a logical plan, optimizes it into a physical plan, and then splits the plan into tasks that are distributed to the executor nodes.
As per Databricks and Spark documentation:
"The driver node is responsible for maintaining information about the Spark application, responding to a user's program or input, and analyzing, distributing, and scheduling work across the executors." This means:
Option A is correct because the driver schedules and coordinates the job execution.
Option B is incorrect because the driver does more than just UI monitoring.
Option C is incorrect since data and computations are distributed across executor nodes.
Option D is incorrect; results are returned to the driver but not stored long-term by it.
Reference: Databricks Certified Developer Spark 3.5 Documentation # Spark Architecture # Driver vs Executors.


NEW QUESTION # 58
An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
frompyspark.sql.functionsimportbroadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

  • A. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
  • B. It increases the partition size for df1 and df2.
  • C. It ensures that the join happens only when the id values are identical.
  • D. It filters the id values before performing the join.

Answer: A

Explanation:
broadcast(df1) tells Spark to send the small DataFrame (df1) to all worker nodes.
This eliminates the need for shuffling df1 during the join.
Broadcast joins are optimized for scenarios with one large and one small table.
Reference:Spark SQL Performance Tuning Guide - Broadcast Joins


NEW QUESTION # 59
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set forspark.sql.
adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

  • A. A broadcast nested loop join
  • B. A shuffled hash join
  • C. A Cartesian join
  • D. A sort-merge join

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Adaptive Query Execution (AQE) dynamically selects join strategies based on actual data sizes at runtime. If the size of post-shuffle partitions is below the threshold set by:
spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold
then Spark prefers to use a shuffled hash join.
From the Spark documentation:
"AQE selects a shuffled hash join when the size of post-shuffle data is small enough to fit within the configured threshold, avoiding more expensive sort-merge joins." Therefore:
A is wrong - Cartesian joins are only used with no join condition.
B is correct - this is the optimized join for small partitioned shuffle data under AQE.
C and D are used under other scenarios but not for this case.
Final Answer: B


NEW QUESTION # 60
Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

  • A. psdf.to_pyspark()
  • B. psdf.to_dataframe()
  • C. psdf.to_pandas()
  • D. psdf.to_spark()

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Pandas API on Spark (pyspark.pandas) allows interoperability with PySpark DataFrames. To convert apyspark.pandas.DataFrameto a standard PySpark DataFrame, you use.to_spark().
Example:
df = psdf.to_spark()
This is the officially supported method as per Databricks Documentation.
Incorrect options:
B, D: Invalid or nonexistent methods.
C: Converts to a local pandas DataFrame, not a PySpark DataFrame.


NEW QUESTION # 61
A Spark application is experiencing performance issues in client mode because the driver is resource- constrained.
How should this issue be resolved?

  • A. Switch the deployment mode to cluster mode
  • B. Increase the driver memory on the client machine
  • C. Switch the deployment mode to local mode
  • D. Add more executor instances to the cluster

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark's client mode, the driver runs on the local machine that submitted the job. If that machine is resource- constrained (e.g., low memory), performance degrades.
From the Spark documentation:
"In cluster mode, the driver runs inside the cluster, benefiting from cluster resources and scalability." Option A is incorrect - executors do not help the driver directly.
Option B might help short-term but does not scale.
Option C is correct - switching to cluster mode moves the driver to the cluster.
Option D (local mode) is for development/testing, not production.
Final Answer: C


NEW QUESTION # 62
......

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