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
- 278 Questions and Answers
- Free practice exam
- 27 Study Guides
- 40+ curated videos
Suggest exam preparation strategy
- Go through each knowledge domain in order
- In each article have a go at the questions in the test app.
- Read and understand the material in the article.
- 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.
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
New Questions and Answers
Study articles
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…
Amazon Transcribe
Overview Amazon Transcribe converts speech to text, by using Automatic Speech Recognition (ASR) technology which is the same underlying technology used by Amazon Alexa. Transcribe can work with multiple languages and speakers and incorporate custom vocabulary provided by the user. Transcribe can be configured to remove sensitive text, such as PII information or swearing. Video:…
Random Cut Forest Algorithm
The Random Cut Forest Algorithm (RCF) is an unsupervised algorithm which is used to identify anomalies in data. An anomaly is a data point that differs significantly from the bulk of the data. The Random Cut Forest Algorithm provides a score for each data point. A low score indicates the datapoint is similar to the…
How to select a model for a given machine learning problem
To select a model for a given Machine Learning problem we use the information and conclusions from Framing the Problem. A Machine Learning problem can be described with four aspects: The first aspect concerns the format and structure of the data, which could be numeric, images or text. Numeric data is often tabular. The second…
Pluralsight review – AWS Certified Machine Learning Specialty
Contains affiliate links. If you go to Pluralsight’s website and make a purchase I may receive a small payment. The purchase price to you will be unchanged. Thank you for your support. The AWS Certified Machine Learning Specialty learning path from Pluralsight has six high quality video courses taught by expert instructors. Two are introductory…
Amazon Lex
Overview Lex provides natural language chatbot capability. It is based on the same technology as Amazon Alexa. With Lex a user can communicate by voice as part of a conversation to achieve their desired goal or intent. Lex analyses what the user says, this is termed an utterance. From a few text examples Lex can…
IP Insights Algorithm
SageMaker IP Insights Algorithm is used for detecting anomalies in network traffic. It is an unsupervised learning algorithm that is trained on historical data to learn the patterns of normal network usage. In production it can detect anomalies in network usage that may indicate changes in user behaviour, network performance or malicious activity. The IP…
Ingesting data for Machine Learning
These revision notes describe the AWS services used to ingest streaming data for Machine Learning.
SageMaker unsupervised algorithms
There are five SageMaker unsupervised algorithms that process tabular data. Unsupervised Learning algorithms process data that has not been labeled. IP Insights is an anomaly detection algorithm to detect problems and threats in an IR network. K-Means is a clustering algorithm. Object2Vec translates input data to vectors. Principal Component Analysis (PCA) algorithm is used in…
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…
Amazon Rekognition
Amazon Rekognition overview Rekognition is an AWS SageMaker AI service for image recognition. Rekognition identifies objects, people, text, scenes and activities in images and video. Rekognition has both built in recognition capabilities and custom tags which allow you to label objects and people important to your business. When a tag, or label is identified Rekognition…
Kinesis KPL vs API
The Kinesis Producer Library (KPL) and the Kinesis API can both be used to send data to Kinesis Data Streams. The advantage of the KPL is it provides a lot of added features, such as failed transmission handling built in. If you use the Kinesis API you have to code these features yourself. The advantages…
AWS security for machine learning
Security is a vast subject and AWS even have their own Professional level certificate exam on this subject. Using the AWS course: Exam Readiness: AWS Certified Machine Learning – Specialty as a guide these revision notes give an overview of the main AWS security service Identity and Access Management (IAM) and then highlight security features…
Data cleansing and preparation for modeling
Understanding data, cleansing data and dataset generation are important first steps in exploratory data analysis. Every other phase in the Machine Learning process relies on the data being cleaned and prepared. This Study Guide starts with statistical techniques used to help understand the data. Once data is understood it has to be cleaned up so…
The Machine Learning Production Environment
When you launch a Machine Learning solution in production it needs to perform well to provide the business benefit it was designed for. There are two types of performance: This Study Guide focuses on the production environment. The production environment can be assessed using five measures: There are three curated videos in this Study Guide:…
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…
How to evaluate Machine Learning models
Evaluating Machine Learning models is the last stage before deploying a model to production. We evaluate Machine Learning models to confirm that they are performing as expected and that they are good enough for the task they were created for. The evaluation stage is performed after model training is finished. Different techniques are used depending…
Feature Engineering for Machine Learning
Feature Engineering is the process of creating new features from the original ones to make the prediction power of the chosen algorithm more powerful. This article explains the concepts of Feature Engineering and the techniques to use for Machine Learning.
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…
Amazon Personalize
Overview Amazon Personalize draws on features that Amazon incorporates into their own retail website. This includes personalization experiences, including specific product recommendations, personalized product re-ranking, and customized direct marketing. The Amazon Personalize AI service provides personalisation with AWS doing all the heavy lifting of providing the Machine Learning infrastructure to train and deploy the model….
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…
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…
Deploy and operationalize machine learning solutions
This Study Guide describes how to deploy a Machine Learning Model into the production environment and to monitor it once it is deployed. The foundations of a reliable production environment are good Software Management and Software Engineering. The emerging job role of ML Ops, which is derived from Dev Ops, is focused on delivering the…
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
