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

Question # 1

47 of 55.

A data engineer has written the following code to join two DataFrames df1 and df2:

df1 = spark.read.csv("sales_data.csv")

df2 = spark.read.csv("product_data.csv")

df_joined = df1.join(df2, df1.product_id == df2.product_id)

The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.

Which join strategy will Spark use?

A.

Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.

B.

Shuffle join, because AQE is not enabled, and Spark uses a static query plan.

C.

Shuffle join because no broadcast hints were provided.

D.

Broadcast join, as df2 is smaller than the default broadcast threshold.

Question # 2

A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.)

Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A.

df.select("Name").orderBy(df["Name"].asc())

B.

df.select("Name").distinct().orderBy(df["Name"])

C.

df.select("Name").distinct()

D.

df.select("Name").distinct().orderBy(df["Name"].desc())

Question # 3

16 of 55.

A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.

Which two characteristics of Apache Spark's execution model explain this behavior? (Choose 2 answers)

A.

Transformations are executed immediately to build the lineage graph.

B.

The Spark engine optimizes the execution plan during the transformations, causing delays.

C.

Transformations are evaluated lazily.

D.

The Spark engine requires manual intervention to start executing transformations.

E.

Only actions trigger the execution of the transformation pipeline.

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