MLS-C01 Question 5
Single answerA data science team is working on a machine learning project that requires storing large volumes of structured and unstructured data, including text files, images, and sensor data. The storage solution must support integration with Amazon SageMaker for model training, provide low latency for frequent data access, and be cost-effective for long-term storage. Which storage solution should the team choose?
- A
Amazon S3 with S3 Standard storage class
- B
Amazon RDS with a relational database
- C
Amazon Redshift for data warehousing
- D
Amazon DynamoDB for NoSQL storage
Show answer and explanation
Correct answer: A
Explanation
Amazon S3 is the most suitable solution for creating a data repository for machine learning workloads. It supports storing structured and unstructured data, integrates with Amazon SageMaker for ML model training, and provides cost-effective storage options like S3 Standard for frequently accessed data. Other options like Amazon RDS, Redshift, and DynamoDB are not optimized for the specific requirements of this use case.
- A. Correct.
Amazon S3 with S3 Standard storage class is the ideal choice for storing large volumes of structured and unstructured data. It integrates seamlessly with Amazon SageMaker, offers low latency for frequent data access, and is cost-effective for long-term storage.
- B. Incorrect.
Amazon RDS is designed for relational databases and is not optimized for storing unstructured data like images and text files. It is also not as cost-effective for long-term storage compared to Amazon S3.
- C. Incorrect.
Amazon Redshift is a data warehousing solution optimized for analytics and not suitable for storing large-scale unstructured data or integrating with Amazon SageMaker for model training.
- D. Incorrect.
Amazon DynamoDB is a NoSQL database designed for low-latency data access for key-value and document-based data. It is not cost-effective for storing large volumes of unstructured data like images or text files.