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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. 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) 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.
C) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
D) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.

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() with the inferSchema option set to true
B) Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.
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. An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?

A) spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
B) spark.read.orc("/file/test_data.orc").selected("col1", "col2")
C) spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
D) spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")

4. A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

A) Modify the Spark configuration to disable garbage collection
B) Cache large DataFrames to persist them in memory.
C) Optimize the data processing logic by repartitioning the DataFrame.
D) Increase the memory allocated to the Spark Driver.

5. 8 of 55.
A data scientist at a large e-commerce company needs to process and analyze 2 TB of daily customer transaction data. The company wants to implement real-time fraud detection and personalized product recommendations.
Currently, the company uses a traditional relational database system, which struggles with the increasing data volume and velocity.
Which feature of Apache Spark effectively addresses this challenge?

A) Support for SQL queries on structured data
B) In-memory computation and parallel processing capabilities
C) Built-in machine learning libraries
D) Ability to process small datasets efficiently

Solutions: