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%
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.
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.
This article describes free questions and answers available on the internet: Free questions for the AWS Machine Learning exam
New Questions and Answers
SageMaker Sequence-to-Sequence algorithm is used for machine translation of languages. The algorithm takes the input sequence of tokens, for example French words, and outputs the translation as a sequence of English words. As well as translation, Sequence-to-Sequence can be used to summarize a document and convert speech to text. Sequence-to-Sequence is a Supervised Learning algorithm….
Overview Amazon Translate translates text from one language to another. You can translate individual words, phrases, or entire documents. An API is provided, enabling either real-time or batch translation of text from the source language to the target language. Video: What is Amazon Translate? Key features Use cases Translate use cases have three features in…
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…
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…
Problem Framing is a method used to understand, define and prioritize business problems. It is one of the most important phases in Machine Learning that will determine if all the work that is to be done subsequently is perceived to be of use and provides business value. Framing determines what will be observed and what…
These revision notes describe the AWS services used to ingest streaming data for Machine Learning.
Overview Amazon Textract is used to convert scanned documents to text. This includes text in tables and hand written form. When text is extracted it is returned with coordinates that identify a box shaped area on the document. This allows for auditing later since the text can be traced back to a specific area in…
The Neural Topic Model Algorithm (NTM) is used to identify topics in a corpus of documents. NTM uses statistics to group words. The groups are termed Latent Representations because they are identified via word distributions in the documents. The Latent Representations reveal the semantics of the documents and so outperform analysis using the word form…
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…
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…
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,…
Need more practice with the exams? Check out Whizlab’s free test with 15 questions. They also have three practice tests (65 questions each) and five section tests (10-15 questions each). Money off promo codes are below. For the AWS Certified Machine Learning Specialty Whizlabs provides a practice tests, a video course and hands-on labs. These…
Streaming data processing is used when data is continuously being generated and needs to be processed as it arrives. The AWS service for data streaming processing is Kinesis. Kineses comprises of four services each with different capabilities and some that can be used together. As well as Kinesis there is another AWS service that can…
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…
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…
Overview Amazon Comprehend is used to analyse text to reveal insights and relationships in unstructured data. The data can be any type of free form text such as emails or text messages. For sentiment analysis Amazon Comprehend can tell you the overall sentiment of the text identifying if it was favourable to the subject, or…
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…
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…
Ground Truth Overview Amazon SageMaker Ground Truth is a service you can use to manually label data. This provides high quality labelled data in the preprocessing stage to be used to train Supervised Learning models. Training data is sent to AWS and they take care of the rest returning your data with attached labels processed by…
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…
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.
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…
These revision notes describe the AWS services available for storing data in data repositories for use in Machine Learning
The K-Nearest Neighbors Algorithm is used to place data into a category for example in recommendation applications used for recommending products on Amazon, articles on Medium, movies on Netflix, or videos on YouTube. It returns results based on the nearest training data points to the sample datapoint, also called nearest neighbors. The K-Nearest Neighbors algorithm…