Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 33 discussion

I would go for C. The tricky phrase is "near-real-time solution", pointing to Firehouse, but it can't send data to DynamoDB, so it leaves us with C as best option. Kinesis Data Firehose currently supports Amazon S3, Amazon Redshift, Amazon OpenSearch Service, Splunk, Datadog, NewRelic, Dynatrace, Sumologic, LogicMonitor, MongoDB, and HTTP End Point as destinations. https://aws.amazon.com/kinesis/data-firehose/faqs/#:~:text=Kinesis%20Data%20Firehose%20currently%20supports,HTTP%20End%20Point%20as%20destinations.

The answer is C, because Firehose does not suppport DynamoDB and another key word is "data" Kinesis Data Streams is the correct choice. Pay attention to key words. AWS likes to trick you up to make sure you know the services.

One of the tricky phrases is 'near-real-time solutions' because it points to the fact that every time a write is made to a database, it incurs a delay, and then retrieving it with an API call adds another latency. With Kinesis Data Streams, that process is optimized because the intermediary that gives you the ability to write to DynamoDB also provides that data to other services due to the retention period of Kinesis Data Streams.

Ans C. High level difference between the Kinesis and DynamoDB: Kinesis Streams allows production/ consumption of large volumes of data (web data, logs, etc); DynamoDB Streams is a feature local to DynamoDB to track the granular changes to DynamoDB table items. (Note also: data latency for Firehose is 60 seconds or higher; Streams is for custom processing and has sub-second processing latency).

Q: What is a destination in Firehose? A destination is the data store where your data will be delivered. Firehose currently supports Amazon S3, Amazon Redshift, Amazon OpenSearch Service, Splunk, Datadog, NewRelic, Dynatrace, Sumo Logic, LogicMonitor, MongoDB, and HTTP End Point as destinations. https://aws.amazon.com/firehose/faqs/

with multiple consumers and on the fly modification, it seems like the most logical choice

I chose B. The "near real time" is very specific to Kinesis firehose which is a better option anyway. The rest of the answer makes sense too. C is wrong : "sensitive data removed by Lambda & then store transaction data in DynamoDB" , while it continues to say other applications are accessing the transaction data from kinesis Data stream !!

need to know.. 1) Lambda Integration 2) Difference between Real time(Kinesis Data Stream) vs Near Real time(Kinesis Fire House) 3) Firehouse can't target DynamoDB

i think c are bad too, because it isn't near real time.

A: DynamoDB streams are logs, not fit for real-time sharing. B: S3 is not document database, it's BLOB D: S3 and files are not database C: Kinesis + Lambda + DynamoDB is high performance, low latency scalable solution.

Answer-C

Data Stream can handle near-real-time and is able to store to DynamoDB

Kinesis Data Streams stores data for later processing by applications , key difference with Firehose which delivers data directly to AWS services.

Correct answer is C. As some commented already, 'near-real-time' could make you think abut Firehose but its consumers are 3rd-party partners destinations, Amazon S3, Amazon Redshift, Amazon OpenSearch and HTTP endpoint so DynamoDB can't be used in this scenario.

C is the best solution for the following reasons: 1. Real-time Data Stream: To share millions of financial transactions with other apps, you need to be able to ingest data in real-time, which is made possible by Amazon Kinesis Data Streams. 2. Data Transformation: You can cleanse and eliminate sensitive data from transactions before storing them in Amazon DynamoDB by utilizing AWS Lambda with Kinesis Data Streams. This takes care of the requirement to handle sensitive data with care. 3. Scalability: DynamoDB and Amazon Kinesis are both extremely scalable technologies that can manage enormous data volumes and adjust to the workload.

C is the best solution for the following reasons: 1. Real-time Data Stream: To share millions of financial transactions with other apps, you need to be able to ingest data in real-time, which is made possible by Amazon Kinesis Data Streams. 2. Data Transformation: You can cleanse and eliminate sensitive data from transactions before storing them in Amazon DynamoDB by utilizing AWS Lambda with Kinesis Data Streams. This takes care of the requirement to handle sensitive data with care. 3. Scalability: DynamoDB and Amazon Kinesis are both extremely scalable technologies that can manage enormous data volumes and adjust to the workload. 4. Low-Latency retrieval: Applications requiring real-time data can benefit from low-latency retrieval, which is ensured by storing the processed data in DynamoDB.

I picked B. We need to understand how Kinesis Data Warehouse works to answer this question right.