Associate-Developer-Apache-Spark-3.5 무료 덤프문제 온라인 액세스

What is a feature of Spark Connect?

• A.It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
• B.It has built-in authentication
• C.Supports DataFrame, Functions, Column, SparkContext PySpark APIs
• D.It supports only PySpark applications

Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

• A.It can be used to interact with any remote cluster using the REST API
• B.It is primarily used for data ingestion into Spark from external sources
• C.It allows for remote execution of Spark jobs
• D.It provides a way to run Spark applications remotely in any programming language

Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?

• A.WARN, NONE, ERROR, FATAL
• B.ALL, DEBUG, FAIL, INFO
• C.ERROR, WARN, TRACE, OFF
• D.FATAL, NONE, INFO, DEBUG

37 of 55.
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses.
The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

• A.customerDF = customerDF.select("email").alias("username", "domain")
• B.customerDF = customerDF.withColumn("domain", col("email").split("@")[1])
• C.customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))
• D.customerDF = customerDF \
  .withColumn("username", split(col("email"), "@").getItem(0)) \
  .withColumn("domain", split(col("email"), "@").getItem(1))

44 of 55.
A data engineer is working on a real-time analytics pipeline using Spark Structured Streaming.
They want the system to process incoming data in micro-batches at a fixed interval of 5 seconds.
Which code snippet fulfills this requirement?

• A.query = df.writeStream \
  .outputMode("append") \
  .trigger(continuous="5 seconds") \
  .start()
• B.query = df.writeStream \
  .outputMode("append") \
  .trigger(processingTime="5 seconds") \
  .start()
• C.query = df.writeStream \
  .outputMode("append") \
  .trigger(once=True) \
  .start()
• D.query = df.writeStream \
  .outputMode("append") \
  .start()