Exam AWS Certified Solutions Architect - Professional SAP-C02 All Questions

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Exam AWS Certified Solutions Architect - Professional SAP-C02 topic 1 question 114 discussion

Exam question from Amazon's AWS Certified Solutions Architect - Professional SAP-C02

Question #: 114
Topic #: 1

[All AWS Certified Solutions Architect - Professional SAP-C02 Questions]

A company has an on-premises monitoring solution using a PostgreSQL database for persistence of events. The database is unable to scale due to heavy ingestion and it frequently runs out of storage.

The company wants to create a hybrid solution and has already set up a VPN connection between its network and AWS. The solution should include the following attributes:
• Managed AWS services to minimize operational complexity.
• A buffer that automatically scales to match the throughput of data and requires no ongoing administration.
• A visualization tool to create dashboards to observe events in near-real time.
• Support for semi-structured JSON data and dynamic schemas.

Which combination of components will enable the company to create a monitoring solution that will satisfy these requirements? (Choose two.)

A. Use Amazon Kinesis Data Firehose to buffer events. Create an AWS Lambda function to process and transform events.
B. Create an Amazon Kinesis data stream to buffer events. Create an AWS Lambda function to process and transform events.
C. Configure an Amazon Aurora PostgreSQL DB cluster to receive events. Use Amazon QuickSight to read from the database and create near-real-time visualizations and dashboards.
D. Configure Amazon Elasticsearch Service (Amazon ES) to receive events. Use the Kibana endpoint deployed with Amazon ES to create near-real-time visualizations and dashboards.
E. Configure an Amazon Neptune DB instance to receive events. Use Amazon QuickSight to read from the database and create near-real-time visualizations and dashboards.

Show Suggested Answer

Suggested Answer: AD 🗳️

by masetromain at Jan. 15, 2023, 4:38 p.m.

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Comments

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God_Is_Love

Highly Voted 2 years, 5 months ago

Selected Answer: AD

Amazon Kinesis Data Firehose (A) allows you to buffer events in two ways: through buffering size or buffering time. With buffering size, you can configure the maximum size of the buffer in MB or the maximum number of records in the buffer. Once the buffer is full, it will automatically deliver the data to the destination Amazon ES (D) has its ability to receive events from various sources in real-time. Amazon ES can ingest data from a variety of sources, such as Amazon Kinesis Data Firehose, Amazon CloudWatch Logs, and Amazon S3, making it a powerful tool for organizations looking to analyze and visualize real-time streaming data. (Kibana dashboards)

upvoted 14 times

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OCHT

Highly Voted 2 years, 4 months ago

Selected Answer: AD

Option B includes using an Amazon Kinesis data stream to buffer events, which is a valid solution for a streaming data use case. However, it requires more ongoing administration compared to using Amazon Kinesis Data Firehose, which is a fully managed service. Additionally, the use of Amazon Kinesis Data Firehose allows the company to take advantage of built-in data transformation and processing capabilities, which can reduce the amount of code required to implement the solution. Therefore, I selected option A over option B as it better meets the requirement of minimizing operational complexity.

upvoted 12 times

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princajen

Most Recent 2 weeks, 2 days ago

Selected Answer: AD

So What Should You Pick: A or B? Pick A (Firehose) if: You care more about minimizing admin overhead. “Near real-time” (60+ seconds delay) is acceptable. You want simpler architecture (Firehose → OpenSearch). Pick B (Streams) if: You need true real-time ingestion. You’re okay with slightly more complexity/admin. You want more control over how/when data is processed. Let’s Recheck the Clue: "A buffer that automatically scales to match the throughput of data and requires no ongoing administration" This screams Kinesis Firehose (A) — it’s fully managed, auto-scales, and requires no manual configuration like shards.

upvoted 1 times

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Paul123456789

4 months, 3 weeks ago

Selected Answer: BD

AWS Lambda is a source for Amazon Kinesis Data Firehose not a destination https://docs.aws.amazon.com/firehose/latest/dev/create-name.html https://docs.aws.amazon.com/firehose/latest/dev/create-destination.html also, Firehose encountered timeout errors when calling AWS Lambda. The maximum supported function timeout is 5 minutes https://docs.aws.amazon.com/firehose/latest/dev/data-transformation.html correct answer B and D

upvoted 2 times

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albert_kuo

5 months, 2 weeks ago

Selected Answer: BD

Option B (Kinesis Data Streams + Lambda) + Option D (Amazon ES + Kibana): Buffer: Kinesis Data Streams automatically scales and buffers events. Processing: Lambda transforms JSON events and sends them to Amazon ES. Storage: Amazon ES stores semi-structured JSON with dynamic schemas. Visualization: Kibana provides near-real-time dashboards. Managed: All services (Kinesis, Lambda, ES, Kibana) are fully managed. Workflow: Events flow from on-premises via VPN to Kinesis → Lambda → ES → Kibana. Result: Meets all requirements seamlessly.

upvoted 2 times

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GabrielShiao

7 months, 1 week ago

Selected Answer: BD

While most voted AD, I vote BD. I picked B instead of A because you can not use lambda to access the kinesis firehose directly.

upvoted 2 times

albert_kuo

5 months, 2 weeks ago

Agreeed

upvoted 1 times

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amministrazione

11 months, 3 weeks ago

A. Use Amazon Kinesis Data Firehose to buffer events. Create an AWS Lambda function to process and transform events. D. Configure Amazon Elasticsearch Service (Amazon ES) to receive events. Use the Kibana endpoint deployed with Amazon ES to create near-real-time visualizations and dashboards.

upvoted 1 times

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Smart

1 year, 3 months ago

Selected Answer: AD

"A buffer that automatically scales to match the throughput of data and requires no ongoing administration." I think buffer, here, means a solution that will reliably hold information for further successful processing. I don't think it means to buffer and batch process the events so I don't agree with other people's comments in regards to buffer. That said, my concern is with "automatically scales to match the throughput of data". Firehose does it automatically. Kinesis can also do automatically if on-demand mode is chosen. Also, "Support for semi-structured JSON data and dynamic schemas." Dynamic Schemas? Firehose or Data stream don't do that. Firehose does do dynamic partitioning and JSON deserializing. I guess that's what the question meant?

upvoted 1 times

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TonytheTiger

1 year, 4 months ago

Selected Answer: AD

Option A NOT Option B - Amazon Data Firehose buffers incoming streaming data in memory to a certain size (buffering size) and for a certain period of time (buffering interval) before delivering it to the specified destinations. https://docs.aws.amazon.com/firehose/latest/dev/buffering-hints.html

upvoted 1 times

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Dgix

1 year, 5 months ago

Selected Answer: AD

On second thought: A because B requires manual shard configuration.

upvoted 1 times

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Dgix

1 year, 5 months ago

Selected Answer: BE

Also, Streams is more real-time.

upvoted 1 times

AWSum1

10 months, 2 weeks ago

It says "Near Real Time" not "Real Time" so Firehouse is the better option between the 2

upvoted 1 times

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Dgix

1 year, 5 months ago

Selected Answer: BD

B rather than A because B integrates the lambda functionality for transformation of the data, which must be done as an added step in A, thereby increasing operational overhead.

upvoted 2 times

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8608f25

1 year, 6 months ago

Selected Answer: AD

A. Use Amazon Kinesis Data Firehose to buffer events. Create an AWS Lambda function to process and transform events.Amazon Kinesis Data Firehose provides a fully managed service for effortlessly loading streaming data into AWS services such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. It scales automatically to match the throughput of data and requires no ongoing administration. AWS Lambda can be used in conjunction with Kinesis Data Firehose to process and transform the data before it’s loaded into the destination, supporting dynamic schemas and semi-structured JSON data. Additionally, Amazon Kinesis Data Firehose has built-in buffering capabilities and can be used to observe events in near-real time, making it a more appropriate choice for the given scenario.

upvoted 2 times

8608f25

1 year, 6 months ago

D. Configure Amazon Elasticsearch Service (Amazon ES) to receive events. Use the Kibana endpoint deployed with Amazon ES to create near-real-time visualizations and dashboards. Amazon Elasticsearch Service (Amazon ES) is a managed service that makes it easy to deploy, secure, operate, and scale Elasticsearch to search, analyze, and visualize data in real-time. Kibana is an open-source visualization tool designed to work with Elasticsearch, providing powerful and easy-to-use features to create dashboards that can visualize data in near-real-time.

upvoted 1 times

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AimarLeo

1 year, 6 months ago

ElasticSearch is the ex name of new OpenSearch

upvoted 2 times

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ninomfr64

1 year, 7 months ago

Selected Answer: BD

I choose Data Stream (KDS) over Data Firehose (KDF) in this scenario: - KDS allows to you store events up to 1 year, allowing to achieve buffering with no constraints on size and with a very large time limit. KDS support on-demand capacity mode - KDF transport mechanism is based on buffering, but here buffering is limited on size (max 128MiB) and time (up to 900 sec)

upvoted 1 times

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career360guru

1 year, 8 months ago

Selected Answer: AD

A and D

upvoted 1 times

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AMohanty

1 year, 11 months ago

BD Question states near-Real time Thats the differentiating factor between Kinesis data stream and Firehose I would go for B and D

upvoted 2 times

chikorita

1 year, 11 months ago

but about "• Managed AWS services to minimize operational complexity." i believe Kinesis Firehose is managed solution whereas DataStream required operational overhead

upvoted 2 times

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