AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 28

An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.
Which solution will meet these requirements?

Answer options

• A. Use Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
• B. Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
• C. Use Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
• D. Use Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.

Correct answer: B

Explanation

The correct answer is B because AWS Glue is specifically designed for data ingestion, allowing for efficient ETL processes with data stored in Amazon S3. While Amazon SageMaker Studio Classic is suitable for model deployment, the other options either do not provide an effective data ingestion solution or are not optimized for this scenario.