Data Engineering Distributed Data Processing Questions and Answers

Question 1

A financial services company needs to process massive volumes of transactional data.
The highest priority is to ensure that all nodes in the distributed system have the same data at the same time to prevent inconsistencies like double-spending.
During a network partition, which two characteristics should their distributed database prioritize according to the CAP theorem?

Accepted Answer

Consistency and Partition Tolerance

Answer

Consistency and Availability

Answer

Availability and Partition Tolerance

Answer

Consistency and Scalability

Question 2

A data engineering team is designing a new analytics platform. They need to support both real-time fraud detection alerts and complex, end-of-day reporting that requires accurate, comprehensive historical data. Which data processing architecture is specifically designed to handle these dual requirements by combining batch and stream processing paths?

Accepted Answer

Lambda Architecture

Answer

Kappa Architecture

Answer

Microservices Architecture

Answer

Monolithic Architecture

Question 3

When comparing Apache Spark and Hadoop MapReduce for a big data project, which of the following is a primary advantage of using Spark?

Accepted Answer

Superior performance for iterative algorithms and interactive queries.

Answer

Lower cost due to reliance on disk storage.

Answer

A more mature and robust security model out-of-the-box.

Answer

Designed exclusively for linear, batch processing of extremely large datasets.

Question 4

A data engineer is tasked with designing a partitioning strategy for a large, distributed user database. The most common query pattern is retrieving a user's complete profile using their `user_id`. To ensure an even distribution of data across nodes and prevent hotspots, which partitioning strategy would be most appropriate?

Accepted Answer

Hash Partitioning

Answer

Range Partitioning

Answer

Vertical Partitioning

Answer

List Partitioning

Question 5

Which of the following is a core mechanism for achieving fault tolerance in a distributed data processing system?

Accepted Answer

Data replication

Answer

Data compression

Answer

Data normalization

Answer

Query optimization

Question 6

A team is building a distributed system and has chosen a design that prioritizes Availability and Partition Tolerance (AP) from the CAP theorem. What is the most likely consequence for their system during a network partition?

Accepted Answer

The system may serve stale or outdated data.

Answer

The system will become entirely unresponsive to all requests.

Answer

All read and write operations will be temporarily blocked.

Answer

The system will guarantee that every client reads the most recent write.