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

1. A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A) Uses trigger() - default micro-batch trigger without interval.
B) Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
C) Uses trigger(continuous='5 seconds') - continuous processing mode.
D) Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.

2. A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

A) .format("parquet")
.option("location", "path/to/destination/dir")
B) CopyEdit
.option("format", "parquet")
.option("destination", "path/to/destination/dir")
C) .option("format", "parquet")
.option("location", "path/to/destination/dir")
D) .format("parquet")
.option("path", "path/to/destination/dir")

3. 20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?

A) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
B) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
C) cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
D) persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.

4. A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

A) Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
B) Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
C) Use spark.read.json() with the inferSchema option set to true
D) Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.

5. 29 of 55.
A Spark application is experiencing performance issues in client mode due to the driver being resource-constrained.
How should this issue be resolved?

A) Add more executor instances to the cluster.
B) Switch the deployment mode to cluster mode.
C) Switch the deployment mode to local mode.
D) Increase the driver memory on the client machine.

Solutions: