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How to earn the AWS Machine Learning Specialty Certification

Sarvech Qadir

The Machine Learning Specialty Certification by Amazon Web Services (AWS), validates your ability to build, train, tune, and deploy machine learning models using the AWS Cloud.

This exam tests one’s understanding of Machine Learning theory. You do not have to be a very experienced AWS developer who knows the full depth and usage of all AWS products. However, it is vital to have an in-depth understanding of AWS and the cloud.

Exam structure

  1. The exam is three hours long
  2. The exam consists of 65 multiple-choice and multi-selection questions
  3. You will be notified after once the time is up if you passed or not
  4. The exam is divided into four ‘domains’. Let’s have a look at each one of them.

Data engineering

This domain sums for 20 percent of the exam. It covers a few AWS services and streaming tools such as Kinesis Firehose, Kinesis Data Streams, Kinesis Analytics, S3, DynamoDB, RDS, and other products in the AWS Analytics stack like Glue and Athena. It may not be that important to understand these tools in great detail, however, you are expected to know what they are and where they are used.

Exploratory Data Analysis (EDA)

This domain sums for 24 percent of the exam. It covers some basic tools such as data cleaning and feature engineering. In this module, you’ll be asked questions about one-hot encoding, normalization, handling missing values, data visualization, etc.


This domain sums for 36 percent of the exam and is one of the most important parts. You are required to have a key understanding of SageMaker algorithms along with their uses. Moreover, you should know how to incorporate other frameworks such as Spark/Tensorflow into SageMaker. This module also tests on machine learning theory outside of AWS. Therefore, knowledge about model tuninge.g., overfitting and underfitting, and concepts of regularisation, dropout, learning rates, and gradient descent should be thoroughly revised along with their implementation. Moreover, you need to understand model evaluation techniques and model metricssuch as accuracy, sensitivity, specificity, recall, and F1. Lastly, you should be able to apply optimization techniques for deep learning models.

Machine Learning Implementation and Operations

This domain sums for 20 percent of the exam. It covers a few of AWS servicessuch as Lex and Comprehend, and you are required to know how to deploy models from SageMaker.

A few resources

Take a look at the following resources while preparing for you exam:

  1. Official exam guide
  2. Amazon Sagemaker
  3. AWS Machine Learning Blog




Sarvech Qadir
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