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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 building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?

A) query = streaming_df.writeStream \
.format("console") \
.outputMode("complete") \
.start()
B) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.start()
C) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.option("checkpointLocation", "/path/to/checkpoint") \
.start()
D) query = streaming_df.writeStream \
.format("console") \
.option("checkpoint", "/path/to/checkpoint") \
.outputMode("append") \
.start()

2. A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

A) Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
B) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
C) Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
D) Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.

3. A data engineer writes the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

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

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

A) Same number as the cluster executors
B) 1
C) 20
D) 10

5. A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?

A) Set the number of partitions to a fixed value, such as 200
B) Set the number of partitions equal to the total number of CPUs in the cluster
C) Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
D) Set the number of partitions equal to the number of nodes in the cluster

Solutions: