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Exam AWS Certified Data Engineer - Associate DEA-C01 All Questions

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Exam AWS Certified Data Engineer - Associate DEA-C01 topic 1 question 27 discussion

A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real-time analysis.
  • B. Access the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. Refresh the materialized views regularly to query the most recent stream data.
  • C. Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Create a materialized view to read data from the stream. Set the materialized view to auto refresh.
  • D. Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.
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Suggested Answer: C 🗳️

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blackgamer
Highly Voted 11 months, 3 weeks ago
Selected Answer: C
The answer is C. It can provide near real-time insight analysis. Refer the article from AWS - https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/
upvoted 8 times
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helpaws
Highly Voted 1 year ago
Selected Answer: C
Key word here is near real-time. If it's involve S3 and COPY, it's not gonna be near real-time
upvoted 7 times
markill123
6 months ago
Redshift cannot create external schemas that map directly to Kinesis Data Streams. You would still need an intermediary step, such as Firehose or S3, to handle data ingestion. Additionally, maintaining auto-refreshing materialized views directly from a stream isn't feasible with Redshift.
upvoted 5 times
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MephiboshethGumani
Most Recent 1 day, 20 hours ago
Selected Answer: D
Creating an external schema and using materialized views directly on top of Kinesis Data Streams is also not an ideal choice because this approach can add complexity and doesn't leverage fully managed solutions like Kinesis Data Firehose. The manual management of data refresh rates adds operational overhead.
upvoted 1 times
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Eltanany
4 days ago
Selected Answer: C
Refer to the article from AWS - https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/
upvoted 1 times
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jesusmoh
1 week, 6 days ago
Selected Answer: D
option D provides a streamlined, efficient, and low-overhead approach to achieving real-time analytics with the specified technologies.
upvoted 1 times
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plutonash
2 months ago
Selected Answer: D
A: Kinesis Data Streams to stage data in Amazon S3. not really easy, B: sql directly to Kinesis Data Streams : functionality not exist C : external schema from redshift to Kinesis Data Streams : functionality not exist D : near real-time = Kinesis Data Firehose
upvoted 2 times
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subbie
2 months, 1 week ago
Selected Answer: C
https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/
upvoted 1 times
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subbie
2 months, 1 week ago
Selected Answer: B
https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/
upvoted 1 times
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haby
2 months, 3 weeks ago
Selected Answer: A
A for me C - Redshift does not natively support direct mapping to Kinesis Data Streams. Some extra configs are needed. D - There will be a 60s latency when using Firehose, so it's "Near" real time not real time.
upvoted 1 times
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HagarTheHorrible
2 months, 3 weeks ago
Selected Answer: D
Redshift does not natively support direct mapping to Kinesis Data Streams. Materialized views cannot directly query streaming data from Kinesis.
upvoted 1 times
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altonh
3 months, 1 week ago
Selected Answer: C
See https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion-getting-started.html
upvoted 1 times
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Asen_Cat
4 months, 1 week ago
Selected Answer: D
D could be the most standard way to handle this case. How to use C to implement it is questionable for me.
upvoted 2 times
Asmunk
3 months, 4 weeks ago
https://docs.aws.amazon.com/streams/latest/dev/using-other-services-redshift.html
upvoted 1 times
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heavenlypearl
4 months, 1 week ago
Selected Answer: C
Amazon Redshift can automatically refresh materialized views with up-to-date data from its base tables when materialized views are created with or altered to have the autorefresh option. Amazon Redshift autorefreshes materialized views as soon as possible after base tables changes. https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-refresh.html
upvoted 1 times
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royalrum
4 months, 2 weeks ago
Firehose is Near-Real time, you can set your buffer size and stream to either Redshift or S3 directly. Since Redshift is not in the option, use s3...
upvoted 1 times
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Shatheesh
4 months, 3 weeks ago
Selected Answer: D
Kinesis Data Streams , option D using Kinesis Data Firehose is a fully managed service that automatically handles the ingestion of data
upvoted 1 times
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markill123
6 months ago
Selected Answer: D
Here’s why D is the best choice: Kinesis Data Firehose is a fully managed service that automatically handles the ingestion of data from Kinesis Data Streams and stages it in S3, which significantly reduces operational overhead compared to managing custom data ingestion pipelines. S3 as a staging area: Using Amazon S3 as a staging location allows for flexible data management, high durability, and direct loading into Redshift without needing to manage complex buffering or data handling processes. COPY command: The COPY command in Amazon Redshift is highly optimized for loading large datasets efficiently, making it a common and effective method to load bulk data from S3 into Redshift for near real-time analysis. Firehose to Redshift: Firehose can automatically buffer, batch, and transform data before loading it into Redshift, reducing manual intervention and ensuring data is readily available for real-time analytics.
upvoted 4 times
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shammous
6 months, 1 week ago
Selected Answer: D
Option C has an issue: Redshift does not natively support direct querying or mapping of Kinesis Data Streams. D is the only correct option.
upvoted 2 times
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