MLS-C01 Question 20
Select 2You are building a machine learning pipeline to analyze real-time data streaming from IoT sensors deployed in multiple locations. The data needs to be ingested, pre-processed, and stored for both immediate analysis and long-term storage. The solution must be scalable, fault-tolerant, and capable of handling high-throughput data streams. Which combination of AWS services should you use to meet these requirements?
- A
Amazon Kinesis Data Streams for real-time ingestion, AWS Lambda for pre-processing, and Amazon S3 for long-term storage
- B
Amazon SQS for real-time ingestion, Amazon RDS for pre-processing, and Amazon S3 for long-term storage
- C
Amazon Kinesis Data Firehose for real-time ingestion and delivery to Amazon S3, and AWS Glue for pre-processing
- D
Amazon Kinesis Data Streams for real-time ingestion, Amazon EMR for real-time analysis, and Amazon Redshift for long-term storage
- E
Amazon Kinesis Data Streams for real-time ingestion, AWS Lambda for pre-processing, and Amazon DynamoDB for long-term storage
Show answer and explanation
Correct answers: A, C
Explanation
To implement a robust and scalable data ingestion solution for an IoT use case, you need services that support high-throughput real-time ingestion (e.g., Amazon Kinesis Data Streams or Kinesis Data Firehose), pre-processing capabilities (e.g., AWS Lambda or AWS Glue), and cost-effective long-term storage (e.g., Amazon S3). Option 1 and Option 3 represent valid combinations of AWS services that meet these requirements, while the other options either misuse services or lack the scalability and cost-effectiveness needed for this scenario.
- A. Correct.
Correct: Amazon Kinesis Data Streams provides a scalable real-time ingestion solution, AWS Lambda can be used for lightweight, serverless pre-processing, and Amazon S3 is an ideal choice for cost-effective, long-term storage.
- B. Incorrect.
Incorrect: Amazon SQS is not designed for high-throughput real-time data ingestion. Amazon RDS is not typically used for pre-processing large volumes of streaming data.
- C. Correct.
Correct: Amazon Kinesis Data Firehose simplifies real-time ingestion and delivers data directly to Amazon S3. AWS Glue can then be used to automate data pre-processing and transformations.
- D. Incorrect.
Incorrect: While Amazon Kinesis Data Streams and Amazon EMR are suitable for real-time ingestion and analysis, Amazon Redshift is optimized for analytical queries rather than long-term, cost-effective storage.
- E. Incorrect.
Incorrect: Amazon DynamoDB is optimized for low-latency, high-throughput transactional workloads, not long-term storage. It is not cost-effective for storing large volumes of streaming IoT data.