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

1. 32 of 55.
A developer is creating a Spark application that performs multiple DataFrame transformations and actions. The developer wants to maintain optimal performance by properly managing the SparkSession.
How should the developer handle the SparkSession throughout the application?

A) Use a single SparkSession instance for the entire application.
B) Avoid using a SparkSession and rely on SparkContext only.
C) Create a new SparkSession instance before each transformation.
D) Stop and restart the SparkSession after each action.

2. 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) 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.
B) Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
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.

3. 2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.json("path/to/file")
B) df.write.mode("overwrite").json("path/to/file")
C) df.write.option("overwrite").json("path/to/file")
D) df.write.mode("append").json("path/to/file")

4. 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 equal to the number of nodes in the cluster
B) Set the number of partitions to a fixed value, such as 200
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 total number of CPUs in the cluster

5. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The operation will fail if the Pandas DataFrame exceeds 1000 rows
B) Data will be lost during conversion
C) The operation will load all data into the driver's memory, potentially causing memory overflow
D) The conversion will automatically distribute the data across worker nodes

Solutions: