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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 is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:

Which of the following code snippets will read all the data within the directory structure?

A) df = spark.read.parquet("/path/events/data/")
B) df = spark.read.parquet("/path/events/data/*")
C) df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
D) df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")

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

3. 6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?

A) Driver Nodes
B) CPU Cores
C) Worker Nodes
D) Executors

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

5. 11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

A) spark.executor.cores
B) spark.task.maxFailures
C) spark.executor.memory
D) spark.sql.shuffle.partitions

Solutions: