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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q28-Q33):

NEW QUESTION # 28
The following code fragment results in an error:

Which code fragment should be used instead?

Answer: A

NEW QUESTION # 29
A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

A. Increase the size of the dataset to create more partitions
B. Reduce the value of spark.sql.shuffle.partitions
C. Enable dynamic resource allocation to scale resources as needed
D. Increase the value of spark.sql.shuffle.partitions

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The number of tasks is controlled by the number of partitions. By default,spark.sql.shuffle.partitionsis 200. If stages are showing very few tasks (less than total cores), you may not be leveraging full parallelism.
From the Spark tuning guide:
"To improve performance, especially for large clusters, increasespark.sql.shuffle.partitionsto create more tasks and parallelism." Thus:
A is correct: increasing shuffle partitions increases parallelism
B is wrong: it further reduces parallelism
C is invalid: increasing dataset size doesn't guarantee more partitions D is irrelevant to task count per stage Final Answer: A

NEW QUESTION # 30
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("age", "salary", ascending=[True, False]).show()
B. df.orderBy(col("age").asc(), col("salary").asc()).show()
C. df.sort("age", "salary", ascending=[False, True]).show()
D. df.sort("age", "salary", ascending=[True, True]).show()

Answer: A

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 # 31
A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?

A. query = streaming_df.writeStream
.format("console")
.outputMode("append")
.start()
B. query = streaming_df.writeStream
.format("console")
.option("checkpoint", "/path/to/checkpoint")
.outputMode("append")
.start()
C. query = streaming_df.writeStream
.format("console")
.outputMode("append")
.option("checkpointLocation", "/path/to/checkpoint")
.start()
D. query = streaming_df.writeStream
.format("console")
.outputMode("complete")
.start()

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To enable fault tolerance and ensure that Spark can resume from the last committed offset after failure, you must configure a checkpoint location using the correct option key:"checkpointLocation".
From the official Spark Structured Streaming guide:
"To make a streaming query fault-tolerant and recoverable, a checkpoint directory must be specified using.
option("checkpointLocation", "/path/to/dir")."
Explanation of options:
Option A uses an invalid option name:"checkpoint"(should be"checkpointLocation") Option B is correct: it setscheckpointLocationproperly Option C lacks checkpointing and won't resume after failure Option D also lacks checkpointing configuration Reference: Apache Spark 3.5 Documentation # Structured Streaming # Fault Tolerance Semantics

NEW QUESTION # 32
A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

A. .trigger(continuous=True)
B. .trigger(continuous='1 second')
C. .trigger(availableNow=True)
D. .trigger(processingTime='1 second')

Answer: D

Explanation:
Comprehensive and Detailed Explanation:
Exactly-once guarantees in Spark Structured Streaming require micro-batch mode (default), not continuous mode.
Continuous mode (.trigger(continuous=...)) only supports at-least-once semantics and lacks full fault- tolerance.
trigger(availableNow=True)is a batch-style trigger, not suited for low-latency streaming.
So:
Option A uses micro-batching with a tight trigger interval # minimal latency + exactly-once guarantee.
Final Answer: A

NEW QUESTION # 33
......

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