Data Engineering Distributed Data Processing 3

Question 1

Which delivery guarantee ensures each message is processed once with no duplicates or loss?

Accepted Answer

Exactly-once

Answer

At-least-once

Answer

At-most-once

Answer

Best-effort

Question 2

In Kafka, what determines the maximum parallelism of consumers in a group?

Accepted Answer

The number of partitions

Answer

The number of brokers

Answer

The replication factor

Answer

The retention period

Question 3

What is the main trade-off described by the CAP theorem during a network partition?

Accepted Answer

Choosing between consistency and availability

Answer

Choosing between speed and storage

Answer

Choosing between SQL and NoSQL

Answer

Choosing between batch and stream

Question 4

A windowed aggregation over event-time streams typically needs which mechanism to handle late data?

Accepted Answer

Watermarks

Answer

Replication factor

Answer

Bloom filters

Answer

Columnar storage

Question 5

Why might increasing the number of partitions improve throughput up to a point?

Accepted Answer

More partitions allow more parallel tasks

Answer

It reduces data size

Answer

It removes the shuffle stage

Answer

It guarantees ordering

Question 6

What is a coordinator's role in a two-phase commit protocol?

Accepted Answer

Asking participants to prepare, then commit or abort

Answer

Storing all data permanently

Answer

Encrypting the network

Answer

Partitioning the input

Question 7

Which storage format is best suited for analytical column scans across distributed engines?

Accepted Answer

Parquet

Answer

CSV

Answer

Plain text logs

Answer

XML