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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q11-Q16):

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

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

Answer: D

Explanation:
The Spark configuration spark.executor.cores defines how many concurrent tasks can be executed within a single executor process.
Each executor is assigned a number of CPU cores.
Each core executes one task at a time.
Therefore, increasing spark.executor.cores allows an executor to run more tasks concurrently.
Example:
--conf spark.executor.cores=4
→ Each executor can run 4 parallel tasks.
Why the other options are incorrect:
B (spark.task.maxFailures): Sets retry attempts for failed tasks.
C (spark.executor.memory): Sets executor memory, not concurrency.
D (spark.sql.shuffle.partitions): Defines number of shuffle partitions, not executor concurrency.
Reference:
Spark Configuration Guide - Executor cores, tasks, and parallelism.
Databricks Exam Guide (June 2025): Section "Apache Spark Architecture and Components" - executor configuration, CPU cores, and parallel task execution.

NEW QUESTION # 12
A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.
How should this issue be resolved?

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

Answer: C

Explanation:
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 # 13
A DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order.
Which code snippet meets the requirement of the developer?

A. df.orderBy("age", "salary", ascending=[True, False]).show()
B. df.orderBy(col("age").asc(), col("salary").asc()).show()
C. df.sort("age", "salary", ascending=[True, True]).show()
D. df.sort("age", "salary", ascending=[False, True]).show()

Answer: A

Explanation:
To sort a PySpark DataFrame by multiple columns with mixed sort directions, the correct usage is:
python
CopyEdit
df.orderBy("age", "salary", ascending=[True, False])
age will be sorted in ascending order
salary will be sorted in descending order
The orderBy() and sort() methods in PySpark accept a list of booleans to specify the sort direction for each column.
Documentation Reference: PySpark API - DataFrame.orderBy

NEW QUESTION # 14
What is the behavior for functiondate_sub(start, days)if a negative value is passed into thedaysparameter?

A. The number of days specified will be added to the start date
B. The same start date will be returned
C. An error message of an invalid parameter will be returned
D. The number of days specified will be removed from the start date

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The functiondate_sub(start, days)subtracts the number of days from the start date. If a negative number is passed, the behavior becomes a date addition.
Example:
SELECT date_sub('2024-05-01', -5)
-- Returns: 2024-05-06
So, a negative value effectively adds the absolute number of days to the date.
Reference: Spark SQL Functions # date_sub()

NEW QUESTION # 15
A data engineer wants to create a Streaming DataFrame that reads from a Kafka topic called feed.

Which code fragment should be inserted in line 5 to meet the requirement?
Code context:
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.[LINE 5]
.load()
Options:

A. .option("subscribe", "feed")
B. .option("kafka.topic", "feed")
C. .option("subscribe.topic", "feed")
D. .option("topic", "feed")

Answer: A

Explanation:
To read from a specific Kafka topic using Structured Streaming, the correct syntax is:
python
CopyEdit
.option("subscribe", "feed")
This is explicitly defined in the Spark documentation:
"subscribe - The Kafka topic to subscribe to. Only one topic can be specified for this option." (Source: Apache Spark Structured Streaming + Kafka Integration Guide)
"subscribe - The Kafka topic to subscribe to. Only one topic can be specified for this option." (Source: Apache Spark Structured Streaming + Kafka Integration Guide) B . "subscribe.topic" is invalid.
C . "kafka.topic" is not a recognized option.
D . "topic" is not valid for Kafka source in Spark.

NEW QUESTION # 16
......

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Associate-Developer-Apache-Spark-3.5 Pass-Sure Braindumps: Databricks Certified Associate Developer for Apache Spark 3.5 - Python & Associate-Developer-Apache-Spark-3.5 Exam Guide

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q11-Q16):

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