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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Dumps With Exact Questions and Answers

Exact2pass Provides 100% Valid Databricks Certified Associate Developer for Apache Spark 3.5-Python Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam dumps Questions and answers which can helps you to Pass Your Certification Exam in First Attempt.

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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Questions and Answers

Question # 1

A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by themarket_timefield.

Which line of Spark code will produce a Parquet table that meets these requirements?

A.

final_df \

.sort("market_time") \

.write \

.format("parquet") \

.mode("overwrite") \

.saveAsTable("output.market_events")

B.

final_df \

.orderBy("market_time") \

.write \

.format("parquet") \

.mode("overwrite") \

.saveAsTable("output.market_events")

C.

final_df \

.sort("market_time") \

.coalesce(1) \

.write \

.format("parquet") \

.mode("overwrite") \

.saveAsTable("output.market_events")

D.

final_df \

.sortWithinPartitions("market_time") \

.write \

.format("parquet") \

.mode("overwrite") \

.saveAsTable("output.market_events")

Question # 2

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.select(

col("email").substr(0, 5).alias("username"),

col("email").substr(-5).alias("domain")

)

B.

customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \

.withColumn("domain", split(col("email"), "@").getItem(1))

C.

customerDF.withColumn("username", substring_index(col("email"), "@", 1)) \

.withColumn("domain", substring_index(col("email"), "@", -1))

D.

customerDF.select(

regexp_replace(col("email"), "@", "").alias("username"),

regexp_replace(col("email"), "@", "").alias("domain")

)

Question # 3

A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.

Which code snippet the data engineer could use to fulfil this requirement?

A)

B)

C)

D)

Options:

A.

Uses trigger(continuous='5 seconds') – continuous processing mode.

B.

Uses trigger() – default micro-batch trigger without interval.

C.

Uses trigger(processingTime='5 seconds') – correct micro-batch trigger with interval.

D.

Uses trigger(processingTime=5000) – invalid, as processingTime expects a string.

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