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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. 9 of 55.
Given the code fragment:
import pyspark.pandas as ps
pdf = ps.DataFrame(data)
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) pdf.spark()
B) pdf.to_dataframe()
C) pdf.to_spark()
D) pdf.to_pandas()

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

3. 4 of 55.
A developer is working on a Spark application that processes a large dataset using SQL queries. Despite having a large cluster, the developer notices that the job is underutilizing the available resources. Executors remain idle for most of the time, and logs reveal that the number of tasks per stage is very low. The developer suspects that this is causing suboptimal cluster performance.
Which action should the developer take to improve cluster utilization?

A) Increase the value of spark.sql.shuffle.partitions
B) Enable dynamic resource allocation to scale resources as needed
C) Reduce the value of spark.sql.shuffle.partitions
D) Increase the size of the dataset to create more partitions

4. 20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?

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

5. A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

A) Configure the Spark UI to automatically collect maximum times
B) Broadcast a variable to share the maximum time among workers
C) Use an RDD action like reduce() to compute the maximum time
D) Use an accumulator to record the maximum time on the driver

Solutions: