MLS-C01 Question 19
Single answerYou are building a machine learning pipeline to analyze real-time IoT sensor data for predictive maintenance. The sensors generate data at high velocity in JSON format, which needs to be ingested, stored, and processed for training a model. The solution must scale automatically based on traffic and allow streaming data to AWS storage services for further analysis. Which solution should you implement for data ingestion?
- A
Use Amazon Kinesis Data Streams to ingest the data, and configure an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3.
- B
Use AWS Glue to extract the data from the IoT sensors and store it in Amazon Redshift for further processing.
- C
Use Amazon RDS with a custom API to receive the data and store it in a relational database for analysis.
- D
Use Amazon SQS to queue the data and batch process it into Amazon DynamoDB for storage.
Show answer and explanation
Correct answer: A
Explanation
Amazon Kinesis Data Streams is purpose-built for real-time ingestion of high-velocity data, which matches the requirement of IoT sensor data ingestion. It integrates seamlessly with Amazon Kinesis Data Firehose, which can deliver the data to Amazon S3 in a scalable and reliable manner. This solution is also highly scalable and automatically adjusts to handle varying traffic, making it ideal for this use case.
- A. Correct.
This is the correct choice because Amazon Kinesis Data Streams is designed for real-time ingestion of high-velocity data. Kinesis Data Firehose can then deliver the data to Amazon S3 for storage, meeting the requirements for scalability and streaming data.
- B. Incorrect.
AWS Glue is primarily used for ETL jobs to transform data and load it into a data warehouse or data lake. It is not suitable for real-time data ingestion from IoT sensors.
- C. Incorrect.
Amazon RDS with a custom API could work for storing data, but it is not designed for real-time ingestion and scaling automatically for high-velocity data streams.
- D. Incorrect.
Amazon SQS is a message queue service, and while it can manage data flow, it does not natively support streaming ingestion or directly integrate with Amazon S3 for scalable storage.