LATEST DATABRICKS ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 TEST QUESTIONS - ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 DUMPS DISCOUNT

Latest Databricks Associate-Developer-Apache-Spark-3.5 Test Questions - Associate-Developer-Apache-Spark-3.5 Dumps Discount

Latest Databricks Associate-Developer-Apache-Spark-3.5 Test Questions - Associate-Developer-Apache-Spark-3.5 Dumps Discount

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q73-Q78):

NEW QUESTION # 73
A DataFramedfhas columnsname,age, andsalary. The developer needs to sort the DataFrame byagein ascending order andsalaryin descending order.
Which code snippet meets the requirement of the developer?

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

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
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])
agewill be sorted in ascending order
salarywill be sorted in descending order
TheorderBy()andsort()methods in PySpark accept a list of booleans to specify the sort direction for each column.
Documentation Reference:PySpark API - DataFrame.orderBy


NEW QUESTION # 74
What is the benefit of Adaptive Query Execution (AQE)?

  • A. It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
  • B. It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
  • C. It allows Spark to optimize the query plan before execution but does not adapt during runtime.
  • 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:
Comprehensive and Detailed Explanation From Exact Extract:
Adaptive Query Execution (AQE) is a powerful optimization framework introduced in Apache Spark 3.0 and enabled by default since Spark 3.2. It dynamically adjusts query execution plans based on runtime statistics, leading to significant performance improvements. The key benefits of AQE include:
Dynamic Join Strategy Selection: AQE can switch join strategies at runtime. For instance, it can convert a sort-merge join to a broadcast hash join if it detects that one side of the join is small enough to be broadcasted, thus optimizing the join operation .
Handling Skewed Data: AQE detects skewed partitions during join operations and splits them into smaller partitions. This approach balances the workload across tasks, preventing scenarios where certain tasks take significantly longer due to data skew .
Coalescing Post-Shuffle Partitions: AQE dynamically coalesces small shuffle partitions into larger ones based on the actual data size, reducing the overhead of managing numerous small tasks and improving overall query performance .
These runtime optimizations allow Spark to adapt to the actual data characteristics during query execution, leading to more efficient resource utilization and faster query processing times.


NEW QUESTION # 75
A developer initializes a SparkSession:

spark = SparkSession.builder
.appName("Analytics Application")
.getOrCreate()
Which statement describes thesparkSparkSession?

  • A. A SparkSession is unique for eachappName, and callinggetOrCreate()with the same name will return an existing SparkSession once it has been created.
  • B. ThegetOrCreate()method explicitly destroys any existing SparkSession and creates a new one.
  • C. A new SparkSession is created every time thegetOrCreate()method is invoked.
  • D. If a SparkSession already exists, this code will return the existing session instead of creating a new one.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
According to the PySpark API documentation:
"getOrCreate(): Gets an existing SparkSession or, if there is no existing one, creates a new one based on the options set in this builder." This means Spark maintains a global singleton session within a JVM process. Repeated calls togetOrCreate() return the same session, unless explicitly stopped.
Option A is incorrect: the method does not destroy any session.
Option B incorrectly ties uniqueness toappName, which does not influence session reusability.
Option D is incorrect: it contradicts the fundamental behavior ofgetOrCreate().
(Source:PySpark SparkSession API Docs)


NEW QUESTION # 76
Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:

  • A. spark.executor.memory
  • B. spark.executor.cores
  • C. spark.driver.cores
  • D. spark.task.maxFailures

Answer: B

Explanation:
spark.executor.cores determines how many concurrent tasks an executor can run.
For example, if set to 4, each executor can run up to 4 tasks in parallel.
Other settings:
spark.task.maxFailures controls task retry logic.
spark.driver.cores is for the driver, not executors.
spark.executor.memory sets memory limits, not task concurrency.
Reference:Apache Spark Configuration


NEW QUESTION # 77
A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

  • A. Cache large DataFrames to persist them in memory.
  • B. Modify the Spark configuration to disable garbage collection
  • C. Optimize the data processing logic by repartitioning the DataFrame.
  • D. Increase the memory allocated to the Spark Driver.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The message"GC overhead limit exceeded"typically indicates that the JVM is spending too much time in garbage collection with little memory recovery. This suggests that the driver or executor is under-provisioned in memory.
The most effective remedy is to increase the driver memory using:
--driver-memory 4g
This is confirmed in Spark's official troubleshooting documentation:
"If you see a lot ofGC overhead limit exceedederrors in the driver logs, it's a sign that the driver is running out of memory."
-Spark Tuning Guide
Why others are incorrect:
Amay help but does not directly address the driver memory shortage.
Bis not a valid action; GC cannot be disabled.
Dincreases memory usage, worsening the problem.


NEW QUESTION # 78
......

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