Data Engineering Real-Time Streaming Architectures 2

Question 1

In Apache Kafka, what guarantees message ordering?

Accepted Answer

Ordering within a single partition

Answer

Ordering across the entire topic

Answer

Ordering by producer timestamp globally

Answer

Ordering by consumer group

Question 2

What is the primary purpose of a watermark in stream processing?

Accepted Answer

To track progress of event time and handle late data

Answer

To encrypt messages in transit

Answer

To compress the event payload

Answer

To assign partitions to consumers

Question 3

Which delivery semantic ensures a message is processed once and only once, even after failures?

Accepted Answer

Exactly-once

Answer

At-least-once

Answer

At-most-once

Answer

Best-effort

Question 4

A tumbling window in stream processing is characterized by:

Accepted Answer

Fixed-size, non-overlapping intervals

Answer

Fixed-size, overlapping intervals

Answer

Variable-size gaps based on activity

Answer

A single window covering all time

Question 5

What does backpressure refer to in a streaming pipeline?

Accepted Answer

A downstream consumer signaling it cannot keep up with input rate

Answer

A producer flooding extra replicas

Answer

Compression applied before network transit

Answer

Rebalancing partitions across brokers

Question 6

In Kafka, what role does the consumer group play?

Accepted Answer

It distributes partitions among members for parallel consumption

Answer

It stores the topic's replication factor

Answer

It controls broker leader election

Answer

It defines the message serialization format

Question 7

Which architecture uses both a batch layer and a speed layer to serve queries?

Accepted Answer

Lambda architecture

Answer

Kappa architecture

Answer

Star schema

Answer

Medallion architecture