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.
- Free Practice exam
- 25+ Tests
- 193 Questions and Answers
- 27 Study Guides
- 20+ curated videos
Suggest exam preparation strategy
- Go through each knowledge domain in order
- In each article have a go at the 5 to 10 questions in the test app at the begining.
- Read and understand the material in the article.
- Answer the 10 to 20 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 192 questions. Each app is one test containing 5 to 20 questions. You answer all the questions and your results are displayed at the end. 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.
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
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.
- Data Engineering – 20%
- Exploratory Data Analysis – 24%
- Modeling – 36%
- Machine Learning Implementation and Operations – 20%
Resources
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
Infographics
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.
AWS official Study Guide
AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) Exam
This study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. The online resources that accompany this Study Guide include practice exams and assessments, electronic flashcards, and supplementary online resources. (Visit Amazon)
Other resources
This article describes free questions and answers available on the internet: Free questions for the AWS Machine Learning exam
Study articles
SageMaker supervised algorithms
There are five SageMaker supervised algorithms for tabular data. DeepAR Forecasting uses Deep Learning for financial forecasting. Linear Learner is good for regression problems. Factorization Machines can be used for the same purpose, but can handle data with gaps and holes better. K-Nearest Neighbor is good at categorising data. XGBoost can predict if an item…
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…
Semantic Segmentation Algorithm
via Gfycat The Semantic Segmentation algorithm processes images by tagging every single pixel in the image. This fine grained approach enables the information about the shapes of objects and edges to be gathered. A common use case is computer vision. The output of training is a Segmentation Mask which is a RGB or grayscale PNG…
Principal Component Analysis Algorithm
Sometimes data can have large amounts of features, so many that further processing or inference can be hampered. When this occurs Principal Component Analysis Algorithm (PCA), an Unsupervised Learning algorithm, is used to reduce the number of features whilst retaining as much information as possible. This is Feature Engineering. PCA has two modes: Regular and…
Ingesting data for Machine Learning
These revision notes describe the AWS services used to ingest streaming data for Machine Learning.
Power Machine Learning at Scale – Summary
This is a summary of the AWS Power Machine Learning at Scale White Paper which is a 15 page pdf document focusing on High Power Computing (HPC) in AWS. It can be downloaded from here: The list of White Papers for Machine Learning is on the Prepare for Your AWS Certification Exam web page: AWS…
Latent Dirichlet Allocation Algorithm
SageMaker Latent Dirichlet Allocation algorithm (LDA) is an Unsupervised Learning algorithm that groups words in a document into topics. The topics are found by a probability distribution of all the words in a document. LDA can be used to discover topics shared by documents within a text corpus. The number of topics is specified by…
Factorization Machines Algorithm
The Factorization Machines Algorithm has two modes: Classification and Regression. Classification is a binary method that returns either one or zero and a label which is a number. The Regression mode returns the predicted value. Factorization Machines are a good choice for high dimensional, sparse datasets. Common uses are web page click prediction and item…
SageMaker image processing algorithms
There are three built-in SageMaker image processing algorithms. They are all Supervised Learning algorithms and so have to be trained using labelled data. Each one analyzes images in a different way and returns different inference data for downstream processing. SageMaker’s three built-in image processing algorithms each have their own way of visualizing real word objects….
Amazon Study Guide review – AWS Certified Machine Learning Specialty
This Amazon Study Guide review is a review of the official Amazon study guide to accompany the exam. The study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. The online resources that accompany this Study Guide include practice exams and assessments, electronic…
Canary deployment, Blue/Green deployment and A/B testing compared
Overview Canary deployment, Blue/Green deployment and A/B testing are methods to control risk. SageMaker Endpoints support these methods. Canary deployment A Canary release is a very risk averse deployment strategy. It involves directing a small proportion of the live traffic to the new production variant and checking that everything works as expected. The proportion of…
DeepAR Forecasting Algorithm
The SageMaker DeepAR Forecasting Algorithm forecasts how the target time series will evolve based on past performance. AR, which stands for AutoRegression, is a statistical method that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. The forecast is a one dimensional time…
Credits
- Light house photo by Rodrigo Soares on Unsplash, Brain stars by GDJ – Gordon Johnson from Pixabay
- Infographic
- tools by Luis Prado from the Noun Project
- binocular by Eucalyp from the Noun Project
- Factory by iconsphere from the Noun Project
- AWS icons by Amazon Web Services
