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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

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 the market_time field.
Which line of Spark code will produce a Parquet table that meets these requirements?

A) final_df \
.sortWithinPartitions("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
B) final_df \
.sort("market_time") \
.coalesce(1) \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
C) final_df \
.sort("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
D) final_df \
.orderBy("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")

2. 8 of 55.
A data scientist at a large e-commerce company needs to process and analyze 2 TB of daily customer transaction data. The company wants to implement real-time fraud detection and personalized product recommendations.
Currently, the company uses a traditional relational database system, which struggles with the increasing data volume and velocity.
Which feature of Apache Spark effectively addresses this challenge?

A) In-memory computation and parallel processing capabilities
B) Ability to process small datasets efficiently
C) Support for SQL queries on structured data
D) Built-in machine learning libraries

3. 22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?

A) spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")
B) spark.read.option("mergeSchema", True).parquet("/data/parquet/")
C) spark.read.parquet("/data/parquet/").option("mergeAllCols", True)
D) spark.read.parquet("/data/parquet/")

4. A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A) By configuring the option recoveryLocation during writeStream
B) By configuring the option recoveryLocation during the SparkSession initialization
C) By configuring the option checkpointLocation during readStream
D) By configuring the option checkpointLocation during writeStream

5. 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, because AQE is not enabled, and Spark uses a static query plan.
B) Broadcast join, as df2 is smaller than the default broadcast threshold.
C) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
D) Shuffle join because no broadcast hints were provided.

Solutions: