a night time photo of a lighthouse with stars shaped like a brain in the sky to symbolize machine learning

How to use this website

For most people effective learning is active learning. Frequent, targeted, testing can help to keep interest, measure achievements and identify weaknesses requiring further study. This site has 25+ tests with 193 questions and answers to help you pass the AWS Certified Machine Learning Specialty exam.

Suggest exam preparation strategy

  1. Go through each knowledge domain in order
  2. In each article have a go at the questions in the test app.
  3. Read and understand the material in the article.
  4. Answer the questions in the test app at the end of the article. Keep repeating the test until you get them all correct.

The test app

This is the test app to access over 25 tests containing 278 questions. Each app is one test with 5 questions from a test bank. You answer all the questions and your results are displayed at the end. Repeat the test to answer different questions randomly chosen from the test bank. Try to answer these questions about Data Repositories, which is part of the Data Engineering knowledge domain. Don’t worry if you do not get many correct.

a screen shot image of the questions and answer test app
Image of the test question app in the Study Guide Machine Learning data repositories compared

Now read through this article: Machine Learning data repositories compared and answer the questions at the bottom of the page. Keep repeating the test at the end of the article, referring to the subject matter in the article, until you get 100%. remember the questions are not just to test you, they are also teaching you.

When you have completed all the Study Guides you are ready to take the Free Practice Exam which has AWS exam style questions.

About the exam

AWS certified Machine Learning Speciality icon

The AWS Certified Machine Learning Specialty exam is a Professional level certification exam from AWS. The exam is taken by experienced developers, engineers and data scientists who either wish to learn how AWS does Machine Learning, or wish to validate their existing knowledge. The exam is a multiple choice test taken at approved AWS test centres. The MLS-C01 exam is divided into four knowledge domains and sixteen sub-domains. This website has study guides that are organised in the same domain and sub-domain structure providing a learning path for the AWS ML exam. Each sub-domain has it’s own study guide with questions and answers so you can test your knowledge and validate your progress.

Machine Learning exam details

  • Exam title: AWS Certified Machine Learning – Specialty
  • Exam code: MLS-C01
  • Exam cost: $300 USD
  • Number of questions in exam: 65 Multiple choice
  • Number of marks in exam: 100 – 1000
  • Exam pass mark: 750 (pass or fail, no grades)
  • Time allowed in exam: 180 minutes
  • How to book the exam: Register with AWS training and book on-line. https://www.aws.training/Dashboard

Exam content structure

The exam content is divided into four knowledge domains each contributing a different proportion of the exam marks. The exame content is explained with a bit more detail in the Exam Guide from AWS.

  1. Data Engineering – 20%
  2. Exploratory Data Analysis – 24%
  3. Modeling – 36%
  4. Machine Learning Implementation and Operations – 20%
Infographic that shows the domains of the AWS Machine Learning Specialty exam
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Test index

Frequent, targeted, testing can help to keep interest, measure achievements and identify weaknesses requiring further study. There are over 25 tests are embedded in the Study Guides. The Test Index has links to take you to each test.

Curated videos

Videos are a great way to learn. You can use them one at a time to reinforce what you have learnt from the study guides, or binge watch loads to give you an overview of the subject. Each video has been chosen with care to illustrate a specific topic covered by the study guides. Longer videos have a timeline added so you can zero in on the specific information you need. The Videos Index has links to each video.


Infographics brings together information and displays it in an easy to remember way. The Infographics Index lists links to all the infographics so you can add them to your Pinterest account.

Other resources

This article describes free questions and answers available on the internet: Free questions for the AWS Machine Learning exam

New Questions and Answers

Study articles

A photograph of the flight instruments of an aeroplane to symbolize model hyperparameters
Modeling (Domain 3)

Model tuning

Hyperparameters can be thought of as the external controls that influence how the model operates, just as flight instruments control how an aeroplane flies. These values are external to the model and are controlled by the user. They can influence how an algorithm is trained and the structure of the final model. The optimized settings…

A photograph of boys playing Rugby and being lifted up in the air to symbolize the SageMaker XGBoost algorithm
Modeling (Domain 3)

XGBoost Algorithm

XGBoost Algorithm stands for eXtreme Gradient Boosting. XGBoost uses ensemble learning, which is also called boosting. The results of multiple models are grouped together to produce a better fit to the training data. Each decision tree model is added using the prediction errors of previous models to improve the fit to the training data. XGBoost…

Photo of lady with shopping approaching a car symbolizing Object2Vec Algorithm
Modeling (Domain 3)

Object2Vec Algorithm

Object2Vec Algorithm is an Unsupervised Learning algorithm. The algorithm compares pairs of data points and preserves the semantics of the relationship between the pairs. The algorithm creates embeddings that can be used by other algorithms downstream. The embeddings are low-dimensional dense embeddings of high-dimensional objects. Object2Vec can be used for product search, item matching and…

A photograph of a fruit stall in a market with a woman buying fruit. Items in the image have a tag label next to them with the fruit or object name. This symbolizes how the SageMaker image classification algorithm works.
Modeling (Domain 3)

Image Classification Algorithm

The SageMaker Image Classification algorithm can apply multiple labels to an image depending on what objects are identified. Objects are either identified, or not, there are no probability scores. Attributes Problem attribute Description Data types and format Image Learning paradigm or domain Image Processing, Supervised Problem type Image and multi-label classification Use case examples Label/tag…

Modeling (Domain 3)

K-Means Algorithm

The K-Means Algorithm is an Unsupervised Learning algorithm used to find clusters. The clusters are formed by grouping data points that are as similar as possible to each other and different from other data points. The distance between data points are calculated and averaged to form groups. K-Means is used for market segmentation, computer vision,…

a photograph of a burning book held in a hand to symbolize the SageMaker built-in algorithm BlazingText
Modeling (Domain 3)

BlazingText Algorithm

BlazingText is the name AWS calls it’s SageMaker built-in algorithm that can identify relationships between words in text documents. These relationships, which are also called embeddings, are expressed as vectors. The semantic relationship between words is preserved by the vectors which cluster words with similar semantics together. This conversion of words to meaningful numeric vectors…

Machine Learning books on bookshelf
Modeling (Domain 3)

SageMaker text processing algorithms

There are four SageMaker text processing algorithms: BlazingText, LDA, NTM and Sequence-to-sequence. BlazingText converts text to numeric vectors. LDA and NTM identify topics in text documents and Sequence-to-sequence provides machine translation of languages. Each algorithm has it’s own section and embedded video. These revision notes are part of subdomain 3.2 Select the appropriate model(s) for…