Free Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Mock Exam – Practice Online Confidently

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Question 1

A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFramedfwith columnsuser_id,product_id, andpurchase_amountand 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?

Options :

A : df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")

B : df.withColumn("discount", df.purchase_amount * 0.1).select("discount")

C : df.withColumn("purchase_date", current_date()).where("total_purchase > 50")

D : df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)

Answer: D

Question 2

A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?

Options :

A : Use the distinct() transformation to combine similar partitions

B : Use the coalesce() transformation with a lower number of partitions

C : Use the sortBy() transformation to reorganize the data

D : Use the repartition() transformation with a lower number of partitions

Answer: B

Question 3

Which feature of Spark Connect should be considered when designing an application that plans to enable remote interaction with a Spark cluster?

Options :

A : It is primarily used for data ingestion into Spark from external sources.

B : It provides a way to run Spark applications remotely in any programming language.

C : It can be used to interact with any remote cluster using the REST API.

D : It allows for remote execution of Spark jobs.

Answer: D

Question 4

A data engineer is working ona Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

Options :

A : streaming_df. select (countDistinct ("Name") )

B : streaming_df.groupby("Id") .count ()

C : streaming_df.orderBy("timestamp").limit(4)

D : streaming_df.filter (col("count") < 30).show()

Answer: D

Question 5

Given a DataFramedfthat has 10 partitions, after running the code: result = df.coalesce(20) How many partitions will the result DataFrame have?

Options :

A : 10

B : Same number as the cluster executors

C : 1

D : 20

Answer: A