[#2]

A company plans to host its data warehouse application on AWS cloud services. The company has a machine learning (ML) model and wants to use that model within its data warehouse for data forecasting. Which AWS service will meet these requirements?

A. Amazon DynamoDB
B. Amazon RedShift ML
C. Amazon Aurora ML
D. Amazon MemoryDB for Redis

Adequate response:

Explanation:

The question revolves around a company wanting to incorporate a machine learning (ML) model into their data warehouse for data forecasting. AWS offers services that allow integrating machine learning capabilities directly into data processing and storage services.

Amazon DynamoDB: This is a fully managed NoSQL database designed for high performance at any scale, but it is not used for ML integrations within a data warehouse context.
                 

[https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Introduction.html]

Amazon Redshift ML: This service enables users to create and train machine learning models directly within Amazon Redshift, which is AWS’s fully managed data warehouse solution. With Redshift ML, users can use SQL commands to build, train, and deploy ML models without needing to move the data to a separate ML environment. This makes it ideal for ML-driven data forecasting within a data warehouse.

Amazon Redshift machine learning (Amazon Redshift ML) is a robust, cloud-based service that makes it easier for analysts and data scientists of all skill levels to use machine learning technology. You provide the data that you want to train a model, and metadata associated with data inputs to Amazon Redshift. Then Amazon Redshift ML creates models that capture patterns in the input data. You can then use these models to generate predictions for new input data without incurring additional costs.

[https://docs.aws.amazon.com/redshift/latest/dg/machine_learning.html]

Amazon Aurora ML: This service brings machine learning to Aurora (a MySQL and PostgreSQL-compatible relational database). It allows integrating ML models into the database but is focused on transactional workloads rather than a data warehouse.

[https://aws.amazon.com/rds/aurora/machine-learning/]

Amazon MemoryDB for Redis: This is an in-memory database service compatible with Redis, primarily used for caching and real-time applications, not for ML or data forecasting.

[https://docs.aws.amazon.com/memorydb/latest/devguide/what-is-memorydb-for-redis.html]
[https://explore.skillbuilder.aws/learn/course/external/view/elearning/10067/getting-started-with-amazon-memorydb-for-redis]
Key Points for AWS CCP Certification:

Amazon Redshift ML is the appropriate choice when you need to integrate machine learning capabilities into a data warehouse. It provides seamless integration with ML models using Amazon SageMaker for training and deployment without requiring deep ML expertise.

Key Points for AWS CCP Certification:

Amazon Redshift ML is the appropriate choice when you need to integrate machine learning capabilities into a data warehouse. It provides seamless integration with ML models using Amazon SageMaker for training and deployment without requiring deep ML expertise.

The correct answer/response is:

B. Amazon RedShift ML