Exam AWS Certified Big Data - Specialty All Questions

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Exam AWS Certified Big Data - Specialty topic 1 question 30 discussion

Exam question from Amazon's AWS Certified Big Data - Specialty

Question #: 30
Topic #: 1

[All AWS Certified Big Data - Specialty Questions]

A company that manufactures and sells smart air conditioning units also offers add-on services so that customers can see real-time dashboards in a mobile application or a web browser. Each unit sends its sensor information in JSON format every two seconds for processing and analysis. The company also needs to consume this data to predict possible equipment problems before they occur. A few thousand pre-purchased units will be delivered in the next couple of months. The company expects high market growth in the next year and needs to handle a massive amount of data and scale without interruption.
Which ingestion solution should the company use?

A. Write sensor data records to Amazon Kinesis Streams. Process the data using KCL applications for the end-consumer dashboard and anomaly detection workflows.
B. Batch sensor data to Amazon Simple Storage Service (S3) every 15 minutes. Flow the data downstream to the end-consumer dashboard and to the anomaly detection application.
C. Write sensor data records to Amazon Kinesis Firehose with Amazon Simple Storage Service (S3) as the destination. Consume the data with a KCL application for the end-consumer dashboard and anomaly detection.
D. Write sensor data records to Amazon Relational Database Service (RDS). Build both the end-consumer dashboard and anomaly detection application on top of Amazon RDS.

Show Suggested Answer

Suggested Answer: C 🗳️

by mattyb123 at Aug. 11, 2019, 12:38 a.m.

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ariane_tateishi

3 years, 7 months ago

A. Should be the right answer, considering that the Kinesis stream have automatic scale, and considering that KCL is not used for Kinesis firehose. https://aws.amazon.com/pt/blogs/big-data/scaling-amazon-kinesis-data-streams-with-aws-application-auto-scaling/

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MichRox

3 years, 7 months ago

is this an actual exam question? B and D are obviously incorrect, but then we have answer C which can be excluded due to KCL not being an available consumer for FIrehose (and the dashboard being real-time), but at the same time answer A doesn't really sit well with "scaling without interruption" (resharding takes time and if I'm not mistaken the shards are unavailable for some time during the operation). Of the 4, A seems the only viable solution, but far from ideal.

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srirampc

3 years, 7 months ago

based on the context of the question "massive amount of data and scale without interruption. Which ingestion solution should the company use?" it is FH. Would like this to be A but question is on on scale. Adding shard is not done in parallel. It would take long time to add 100 shards, in this aspect there could be a service interruption. C is the answer.

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Bulti

3 years, 7 months ago

Answer A: Spark /KCL does not read from KDF.

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san2020

3 years, 7 months ago

my selection A

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AdamSmith

3 years, 7 months ago

The wording of this question is really weird, real-time analysis implies Kinesis Streams (but you have to manage sharding), while massive scaling without interruption implies Kinesis Firehose since it is a managed service (but the minimum delay is 60 seconds).

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Zinty

3 years, 7 months ago

What did you select ?

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practicioner

3 years, 7 months ago

"that customers can see real-time dashboards" - this is a key for right answer. I think "A", but C would be more appropriate without this phrase

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Percival

3 years, 7 months ago

the difference between fh and kinesis streams is whether managed services or not. fh support autoscaling but kinesis streams need manual processing for autoscaling. 2 seconds interval is not for analytics. it is just for sending data from sensors to kinesis streams. plz do not confuse sending time with processing time. the delay for buffers (60seconds) to S3 is reasonable. it does not hurt a real time processing...

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yuriy_ber

3 years, 7 months ago

you are right about auto scaling, however you can split shards for Kinesis. Another point, you can not consume the data directly from Firehose using KCL (you can aggregate data using KPL but only using Kinesis Data Streams before - https://docs.aws.amazon.com/streams/latest/dev/kpl-with-firehose.html). Another point - what you are going to do after you wrote your data to S3, how to provide real-time capabilities?

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Raju_k

3 years, 7 months ago

I would choose A over C because Firehose has minimum delay of 60 seconds which is not ideal in this case (2 seconds frequency) https://aws.amazon.com/kinesis/data-firehose/faqs/

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