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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q51-Q56):

NEW QUESTION # 51
You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A. DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
B. DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A
C. DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
D. DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself

Answer: B

Explanation:
Comprehensive and Detailed Explanation:
Broadcast joins work by sending the smaller DataFrame to all executors, eliminating the shuffle of the larger DataFrame.
From Spark documentation:
"Broadcast joins are efficient when one DataFrame is small enough to fit in memory. Spark avoids shuffling the larger table." DataFrame B (1 GB) fits within the default threshold and should be broadcasted.
It eliminates the need to shuffle the large DataFrame A.
Final Answer: B

NEW QUESTION # 52
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(processingTime='1 second')
C. .trigger(continuous='1 second')
D. .trigger(availableNow=True)

Answer: B

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 # 53
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A. Data will be lost during conversion
B. The operation will fail if the Pandas DataFrame exceeds 1000 rows
C. The operation will load all data into the driver's memory, potentially causing memory overflow
D. The conversion will automatically distribute the data across worker nodes

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When you convert a largepyspark.pandas(aka Pandas API on Spark) DataFrame to a local Pandas DataFrame using.toPandas(), Spark collects all partitions to the driver.
From the Spark documentation:
"Be careful when converting large datasets to Pandas. The entire dataset will be pulled into the driver's memory." Thus, for large datasets, this can cause memory overflow or out-of-memory errors on the driver.
Final Answer: D

NEW QUESTION # 54
Given the following code snippet inmy_spark_app.py:

What is the role of the driver node?

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

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 # 55
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 ensures that the join happens only when the id values are identical.
B. It filters the id values before performing the join.
C. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
D. It increases the partition size for df1 and df2.

Answer: C

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 # 56
......

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