Serverless Data Lake to MPP Warehouse Solution

SAA-C03 By islever Editorial Last updated

Determine high-performing data ingestion and transformation solutions.

Short answer

C Run a daily AWS Glue job to transform the data and load the data into Amazon Redshift Serverless. Use Amazon Redshift ML to create and train the ML models.

Question

A company has an Amazon S3 data lake. The company needs a solution that transforms the data from the data lake and loads the data into a data warehouse every day. The data warehouse must have massively parallel processing (MPP) capabilities. Data analysts then need to create and train machine learning (ML) models by using SQL commands on the data. The solution must use serverless AWS services wherever possible. Which solution will meet these requirements?
  1. Run a daily Amazon EMR job to transform the data and load the data into Amazon Redshift. Use Amazon Redshift ML to create and train the ML models.
  2. Run a daily Amazon EMR job to transform the data and load the data into Amazon Aurora Serverless. Use Amazon Aurora ML to create and train the ML models.
  3. Run a daily AWS Glue job to transform the data and load the data into Amazon Redshift Serverless. Use Amazon Redshift ML to create and train the ML models. Correct Answer
  4. Run a daily AWS Glue job to transform the data and load the data into Amazon Athena tables. Use Amazon Athena ML to create and train the ML models.

Key Takeaways

The core trap is confusing general-purpose compute (EMR) or non-MPP databases (Aurora/Athena) with the specific requirements for an MPP data warehouse that supports native SQL-based machine learning via Redshift ML.

This question tests the selection of AWS services for a serverless ETL pipeline targeting an MPP data warehouse with SQL-based ML capabilities. The correct solution leverages AWS Glue and Amazon Redshift Serverless.

Common mistake: Candidates often select Option A because EMR is a common tool for data transformation, but they miss the critical constraint to use 'serverless' services wherever possible, making Redshift Serverless superior to standard Redshift in this context.

Expert Analysis

Why the Answer Is Correct

Option C is the only choice that satisfies all constraints: it uses serverless transformation (AWS Glue), loads data into an MPP-capable data warehouse (Amazon Redshift), utilizes the serverless variant of that warehouse (Redshift Serverless) to meet the 'serverless wherever possible' requirement, and enables SQL-based ML training via Amazon Redshift ML.

Why the Other Options Are Wrong

Option A fails because while EMR can transform data, standard Amazon Redshift is not explicitly serverless (though managed, it requires cluster management compared to Redshift Serverless), and the prompt emphasizes serverless options. Option B is incorrect because Amazon Aurora is a relational database, not an MPP data warehouse, and Aurora does not support Redshift ML. Option D is incorrect because Amazon Athena is a query service, not a traditional data warehouse for loading transformed data for complex ML workflows in this context, and Athena does not have a feature called 'Athena ML'.

Community Comment Notes

Community consensus strongly favors Option C. As user Mikado211 noted, "Data warehouse ==> Redshift" and emphasized that "Since the question asks to use serverless as much as possible, Redshift Serverless is a better solution." Another commenter BatVanyo correctly pointed out that neither EMR nor Aurora fit the strict serverless and MPP criteria simultaneously, leaving C as the only viable option.

Exam Strategy

Always map keywords like 'MPP', 'Data Warehouse', and 'SQL-based ML' directly to Amazon Redshift. When 'serverless' is requested, look for 'Serverless' variants of services (e.g., RDS Serverless, Redshift Serverless, Lambda) over their provisioned counterparts.

Frequently Asked Questions

Why is AWS Glue preferred over EMR here?

AWS Glue is fully serverless and managed, fitting the 'serverless wherever possible' constraint better than EMR, which typically involves managing clusters unless EMR Serverless is specified.

What is Amazon Redshift ML?

It allows you to create, train, and deploy machine learning models using standard SQL commands on your Redshift data warehouse without needing other ML tools.

More SAA-C03 FAQ →

Community Votes

C
100%

100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 9 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 9

  1. Mikado211 👍 6 Answered C
    Data warehouse ==> Redshift Without additional informations both EMR and Glue Jobs can work. Since the question asks to use serverless as much as possible, Redshift Serverless is a better solution. C
  2. BatVanyo 👍 5 Answered C
    Neither A, nor B explicitly say "EMR serverless" which is a new AWS offering, so I exclude these two. MPP goes hand in hand with Redshift, so D is also incorrect. This leaves C the only possible serverless option here.
  3. rondelldell 👍 2
    A Amazon EMR Serverless is a deployment option for Amazon EMR that provides a serverless runtime environment. This simplifies the operation of analytics applications that use the latest open-source frameworks, such as Apache Spark and Apache Hive. With EMR Serverless, you don’t have to configure, optimize, secure, or operate clusters to run applications with these frameworks. EMR Serverless helps you avoid over- or under-provisioning resources for your data processing jobs. EMR Serverless automatically determines the resources that the application needs, gets these resources to process your jobs, and releases the resources when the jobs finish. For use cases where applications need a response within seconds, such as interactive data analysis, you can pre-initialize the resources that the application needs when you create the application.
  4. mk168898 👍 2 Answered C
    Data Warehouse => redshift Use AWS Services whereever possible => Redshift serverless
  5. 1dd 👍 1
    EMR works with big data transfer
Show 4 more comments
  1. seetpt 👍 3 Answered C
    C is correct
  2. asdfcdsxdfc 👍 2
    should be C
  3. 1dd 👍 2 Answered C
    Option C
  4. sk1974 👍 1 Answered B
    Any reason it cannot be B ?

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