Cloud Integration test - Big Data Engineer questions

Question 1

What is the advantage of using columnar databases for Big Data?

Columnar databases are only used for transactional workloads. Columnar databases are slower and less efficient than row-based databases. Columnar databases are only used for unstructured data. Columnar databases are only used for processing small datasets. Columnar databases are optimized for analytics workloads and can deliver faster query performance on large datasets compared to traditional row-based databases. Columnar databases are not optimized for analytics workloads.

Correct Answer: Columnar databases are optimized for analytics workloads and can deliver faster query performance on large datasets compared to traditional row-based databases.

Columnar databases are optimized for analytics workloads and can deliver faster query performance on large datasets compared to traditional row-based databases. While they may be slower for transactional workloads, they are generally faster for analytical queries. They are also not limited to processing only small datasets or only unstructured data.

Question 2

What is the purpose of Hadoop Distributed File System (HDFS) in a Big Data ecosystem?

To provide in-memory caching for frequently accessed data in a Hadoop cluster. To provide scalable and fault-tolerant storage for structured data. To provide a centralized repository for cloud-based data integration tools. To perform batch processing of structured data in a Hadoop cluster. To process and analyze data in real-time. To store and manage large amounts of unstructured data across multiple nodes in a Hadoop cluster.

Correct Answer: To store and manage large amounts of unstructured data across multiple nodes in a Hadoop cluster.

Hadoop Distributed File System (HDFS) is the primary storage system used in a Hadoop cluster. It is designed to store and manage large amounts of unstructured data across multiple nodes in a Hadoop cluster. HDFS provides scalable and fault-tolerant storage for Big Data, making it possible to store huge amounts of data in a cost-effective manner. It is optimized for batch processing of large files, which makes it an ideal storage system for Big Data processing, analytics, and machine learning workloads.

Question 3

What is the role of Spark in Big Data processing?

To create and execute workflows for data processing and analysis. To develop and implement machine learning algorithms for data analysis. To automate the deployment and scaling of infrastructure resources. To provide visualization tools for analyzing data. To store and manage large volumes of data in distributed clusters. To process and analyze large data sets in real-time or batch mode.

Correct Answer: To process and analyze large data sets in real-time or batch mode.

Apache Spark is a unified analytics engine for large-scale data processing. It can be used for real-time or batch processing of data, making it useful for a wide range of applications, such as data mining, machine learning, and web analytics. The other options listed are not related to Spark.

Cloud Integration

Big Data Engineer - Cloud Integration Example Questions

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