Online Access Free CDP-3002 Exam Questions

What is the primary advantage of using Apache Spark for distributed processing compared to traditional single-node processing?

• A.Improved data visualization capabilities
• B.Enhanced data security
• C.Increased storage capacity
• D.Faster processing of large datasets

In the context of Spark, what is a potential downside of indiscriminate use of data caching, especially with the MEMORY_AND DISK storage level?

• A.It can decrease network traffic by reducing the need for data shuffling.
• B.It can lead to reduced fault tolerance due to reliance on in-memory storage.
• C.It may increase execution time due to overheads from frequent disk 1/0 operations.
• D.It enhances data security by storing intermediate results in encrypted form.

When leveraging Spark's DataFrame API for caching, what implicit optimization does Spark perform to enhance processing efficiency?

• A.Automatic selection of the optimal storage level based on the DataFrame's size
• B.Automatic conversion of all operations to map-reduce jobs
• C.Inlining of functions to reduce the overhead of JVM method calls
• D.Predicate pushdown and other logical optimizations before physical execution

Which strategy is most effective for managing schema evolution in a big data application that relies on schema inference?

• A.Completely disabling schema inference and relying on static schemas
• B.Integrating schema registry services that track and manage schema versions
• C.Using a single version of the schema for all data, regardless of changes
• D.Manually updating the inferred schema for each data ingestion

How can you utilize Spark SQL for complex data analysis involving joins and aggregations on large datasets?

• A.By implementing custom logic in RDDs for data manipulation.
• B.By using Spark ML libraries for machine learning tasks on data.
• C.By directly writing SQL queries against distributed data in Spark.
• D.Both A and B.