Data Warehousing on AWS Training Data Warehousing on AWS: Architecture Design 4

Question 1

A retail company needs near-real-time data availability in their Redshift warehouse. Which ingestion pattern best meets this requirement?

Accepted Answer

Amazon Kinesis Data Firehose streaming into Redshift

Answer

Kinesis Data Firehose can buffer and load streaming data into Redshift in near real-time with minimal latency.

Question 2

What is the primary benefit of using columnar storage in Amazon Redshift compared to row-based storage?

Accepted Answer

Better compression and reduced I/O for analytical queries that scan few columns

Answer

Columnar storage groups values of the same column together, enabling high compression and limiting I/O to only the columns needed by a query.

Question 3

Which architectural pattern uses Amazon Redshift as the serving layer while keeping raw data in S3 and using AWS Glue for transformation?

Accepted Answer

Lake house architecture

Answer

The lake house architecture integrates a data lake (S3) with a data warehouse (Redshift) using Glue for transformation and Spectrum for in-place querying.

Question 4

In Redshift WLM (Workload Management), what happens to a query when all slots in a queue are occupied?

Accepted Answer

The query waits in the queue until a slot becomes available

Answer

Queries that exceed the queue's concurrency limit wait in the queue until a slot frees up, unless the queue timeout is reached.

Question 5

A data engineer notices that a Redshift table has significant data skew. What is the most likely cause?

Accepted Answer

The distribution key has low cardinality, concentrating data on few slices

Answer

Choosing a distribution key with low cardinality or many repeated values causes rows to pile up on the same slice, creating skew.

Data Warehousing on AWS Training Practice Test

Data Warehousing on AWS Training Practice Test

Data Warehousing on AWS Training Data Warehousing on AWS: Architecture Design 4