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Databricks Certified Associate Developer for Apache Spark 3.5 - Python 認定 Associate-Developer-Apache-Spark-3.5 試験問題 (Q134-Q139):

質問 # 134
A data analyst wants to add a column date derived from a timestamp column.
Options:

A. dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
B. dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
C. dates_df.withColumn("date", f.to_date("timestamp")).show()
D. dates_df.withColumn("date", f.from_unixtime("timestamp")).show()

正解：C

解説：
f.to_date() converts a timestamp or string to a DateType.
Ideal for extracting the date component (year-month-day) from a full timestamp.
Example:
frompyspark.sql.functionsimportto_date
dates_df.withColumn("date", to_date("timestamp"))
Reference:Spark SQL Date Functions

質問 # 135
A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

A. The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
B. The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
C. Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
D. The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.

正解：A

解説：
In Apache Spark, the execution hierarchy is structured as follows:
Application: The highest-level unit, representing the user program built on Spark.
Job: Triggered by an action (e.g., collect(), count()). Each action corresponds to a job.
Stage: A job is divided into stages based on shuffle boundaries. Each stage contains tasks that can be executed in parallel.
Task: The smallest unit of work, representing a single operation applied to a partition of the data.
When the collect() action is invoked, Spark initiates a job. This job is then divided into stages at points where data shuffling is required (i.e., wide transformations). Each stage comprises tasks that are distributed across the cluster's executors, operating on individual data partitions.
This hierarchical execution model allows Spark to efficiently process large-scale data by parallelizing tasks and optimizing resource utilization.

質問 # 136
A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A. df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
B. df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
C. df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
D. df.withColumn("discount", df.purchase_amount * 0.1).select("discount")

正解：C

解説：
Shuffling occurs in operations like groupBy, reduceByKey, or join-which cause data to be moved across partitions. The repartition() operation can also cause a shuffle, but in this context, it follows an aggregation.
In Option D, the groupBy followed by agg results in a shuffle due to grouping across nodes.
The repartition(10) is a partitioning transformation but does not involve a new shuffle since the data is already grouped.
This sequence - shuffle (groupBy) followed by non-shuffling (repartition) - is correct.
Option A does the opposite: the filter does not cause a shuffle, but groupBy does - this makes it the wrong order.

質問 # 137
A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
B. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
C. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
D. The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame

正解：B

解説：
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark, broadcast variables are used to efficiently distribute large, read-only data to all worker nodes. However, broadcasting very large datasets can lead to memory issues on executors if the data does not fit into the available memory.
According to the Spark documentation:
"Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. This can greatly reduce the amount of data sent over the network." However, it also notes:
"Using the broadcast functionality available in SparkContext can greatly reduce the size of each serialized task, and the cost of launching a job over a cluster. If your tasks use any large object from the driver program inside of them (e.g., a static lookup table), consider turning it into a broadcast variable." But caution is advised when broadcasting large datasets:
"Broadcasting large variables can cause out-of-memory errors if the data does not fit in the memory of each executor." Therefore, if the broadcasted DataFrame containing millions of rows exceeds the memory capacity of the executors, the job may fail due to memory constraints.
Reference:Spark 3.5.5 Documentation - Tuning

質問 # 138
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")
.[LINE5]
.load()
Options:

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

正解：D

解説：
Comprehensive and Detailed 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)
B)."subscribe.topic" is invalid.
C)."kafka.topic" is not a recognized option.
D)."topic" is not valid for Kafka source in Spark.

質問 # 139
......

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一番優秀なAssociate-Developer-Apache-Spark-3.5ダウンロード試験-試験の準備方法-有効的なAssociate-Developer-Apache-Spark-3.5認定資格試験問題集

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Associate-Developer-Apache-Spark-3.5認定資格試験問題集 & Associate-Developer-Apache-Spark-3.5試験勉強書

Databricks Certified Associate Developer for Apache Spark 3.5 - Python 認定 Associate-Developer-Apache-Spark-3.5 試験問題 (Q134-Q139):

有難いAssociate-Developer-Apache-Spark-3.5ダウンロード試験-試験の準備方法-実際的なAssociate-Developer-Apache-Spark-3.5認定資格試験問題集

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