A guide to the AWS Machine Learning Engineer - Associate (MLA-C01)

David Blocher is a 13-times AWS certified course author here at Pluralsight. After passing the Machine Learning Engineer exam on the first try, here are his tips on what to expect going into the MLA-C01, and how you can ace it as well.

Both AWS and ML skills are in hot demand, but proving you have them can be difficult. Thankfully, that’s why AWS has a mid-level ML certification called the AWS Machine Learning Engineer - Associate, to solve that very problem!

In this article, I’ll cover all the most common questions you might have about this cert and the exam, including how it’s structured and what to study up on, so you can pass it your first time around.

What is the AWS Machine Learning Engineer - Associate certification?

The AWS Machine Learning Engineer - Associate is an intermediate-level certification designed for cloud engineers who work with Amazon SageMaker, and want to certify their skills in cloud architecture, data engineering, DevOps, and data science as it relates to machine learning on AWS. It’s also perfect for machine learning engineers who want to learn more about maintaining ML ecosystems on AWS.

How hard is the AWS Machine Learning Engineer - Associate exam?

It’s intermediate, as the AWS level suggests. Whether you find the exam difficult or not will largely depend on how much recent, hands-on experience you’ve got with AWS machine learning in general, especially Amazon SageMaker. If you’re also familiar with sitting AWS exams already, you’ll have an easier time going in simply knowing the standardised exam structure.

Who is the AWS Machine Learning Engineer - Associate for?

The Machine Learning Engineer - Associate certification is designed for cloud engineers who work with Amazon SageMaker, and want to certify their skills in cloud architecture, data engineering, DevOps, and data science as it relates to machine learning on AWS. It’s also perfect for machine learning engineers who want to learn more about maintaining ML ecosystems on AWS.

How is the MLA-C01 exam structured?

This exam consists of 85 questions over 170 minutes. It includes the traditional AWS question types—multiple select and multiple choice— as well as three question types called ordering, matching, and case study. If you’re thinking of taking this exam, you should definitely familiarize yourself with these question formats.

Just like other AWS certification exams, you can take this exam at a testing center, or take an online proctored exam from home. Personally, I prefer testing centers so I don't have to worry about technical issues (or clearing off my desk), but it's all up to your personal preference and local availability.

How much does the exam cost?

The exam costs 150 USD, but you can check out AWS’s exam pricing for additional information (E.g. Foreign exchange rates.)

What the AWS Machine Learning Engineer - Associate exam covers

The exam consists of four domains: 

• Data Preparation for Machine Learning

• ML Model Development

• Deployment and Orchestration of ML Workflows

• ML Solution Monitoring, Maintenance, and Security

For a full breakdown of these domains, I highly recommend checking out the AWS exam guide.

Key things to expect when preparing to take the MLA-C01

Study up on SageMaker

An alternate name for this certification could have been the Amazon SageMaker certification. The vast majority of questions on the exam involve SageMaker, which has evolved from a single Machine Learning service to an integrated family of services designed to help you build, train, and deploy machine learning models. SageMaker provides tools for data pipelines, and infrastructure management for training, debugging, hosting, and monitoring machine learning models. 

You'll need to understand the machine learning lifecycle, from data ingestion, data storage, data preparation, model training, tuning, deployment, monitoring, and retraining, and you'll have to do this all while keeping your data secure along the way.

SageMaker Data Wrangler

You'll see a lot of SageMaker Data Wrangler, which is used for data preparation and feature engineering with minimal code writing. You'll need to understand when it's more appropriate to use Data Wrangler as opposed to other data preparation tools such as AWS Glue.

SageMaker Model Registry

You'll also need to know how to sort and manage your various machine learning models using the SageMaker Model Registry. You'll need to know the difference between Model groups and collections.

SageMaker Inference

When it comes to deploying your models, the exam heavily emphasizes using SageMaker inference, which will manage your infrastructure behind endpoints. You'll need to know when it's appropriate to use real-time, serverless, asynchronous, or batch endpoints for your models deployed to SageMaker Inference.

Beyond SageMaker, study your general AI/ML concepts and services

Outside of SageMaker, you'll need to be familiar with general machine learning concepts. You'll need to understand the difference between regression and classification models, and the metrics you would use to tune and monitor those models. You'll be tested on overfitting and underfitting, and the different techniques you can use to address these common problems.

You'll also see several questions relating to stand-alone managed AI and Machine Learning services on AWS. You'll need to know the basics of Amazon Bedrock, which is their fully-managed Generative AI service, including how to fine-tune pre-built models with proprietary data. You'll also see several questions involving Amazon Comprehend, which can be used for sentiment analysis, as well as redacting sensitive data in natural language documents.

Conclusion

This is only a snapshot of the most common services, but the breadth of this exam is far beyond what I've mentioned here. Hands-on experience will be a must for achieving this highly sought-after Machine Learning certification.

Good luck on your learning and certification journey, and keep being awesome!

How to prepare for the AWS Certified Machine Learning Engineer - Associate exam

If you want to study and pass the exam, check out Pluralsight’s dedicated certification path for the AWS Certified Machine Learning Engineer - Associate. By taking the course, you’ll build the knowledge and confidence to ace your exam with a variety of prep tools, including video courses, demos, and practice exams.