AWS Machine Learning Specialty Cheat Sheet

  1. Home
  2. AWS
  3. AWS Machine Learning Specialty Cheat Sheet

As an aspiring AWS Machine Learning Specialty certified professional, you need to have a strong understanding of various AWS services, tools, and techniques related to machine learning. This cheat sheet will provide you with a quick and concise reference guide to the key concepts, terminologies, and best practices that you need to know for the exam.

This cheat sheet is divided into different sections, each covering a specific topic relevant to the AWS Machine Learning Specialty certification exam. You’ll find useful information on AWS services, such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend, as well as on machine learning algorithms, model training, and deployment. Additionally, the cheat sheet includes tips and tricks for optimizing machine learning models, handling data processing and management, and ensuring data security and privacy.

Whether you’re just starting to learn about machine learning on AWS or you’re preparing for the certification exam, this cheat sheet is a valuable resource that will help you to solidify your understanding of the key concepts and prepare you to pass the exam with confidence. So, let’s dive in and start exploring the AWS Machine Learning Specialty Cheat Sheet!

What is AWS Machine Learning Specialty?

AWS Machine Learning specialty certification exam exam is intended for Amazon Web Services. It enables developers to use algorithms to discover patterns in user data, construct mathematical models based on these patterns, and subsequently design and execute predictive applications. This exam verifies a candidate’s ability to use the AWS Cloud to construct, train, tune, and deploy machine learning (ML) models. It assesses a candidate’s ability to create, build, deploy, and manage machine learning (ML) solutions for a variety of business challenges. It will demonstrate that the candidate has the capacity to:

  • Choose and provide valid reasons for selecting the suitable machine learning approach for a specific business issue.
  • Recognize the AWS services that are suitable for implementing machine learning solutions.
  • Create and execute machine learning solutions that are scalable, cost-effective, dependable, and secure.

AWS Machine Learning Specialty Glossary

  1. AWS Machine Learning – A web-based service that enables developers to create and deploy machine learning models on a large scale.
  2. Algorithm – A set of instructions that a machine learning model follows to perform a specific task, such as classification or regression.
  3. AutoML – Automated Machine Learning, a set of tools and techniques that enable developers to automatically build, train, and optimize machine learning models without the need for manual intervention.
  4. Batch inference – The process of using a trained machine learning model to make predictions on a large dataset in one go.
  5. Data preprocessing – The procedure of refining and converting raw data into a format suitable for utilization by a machine learning model.
  6. Deep learning – A form of machine learning that employs artificial neural networks to represent intricate patterns within data.
  7. Ensemble learning – The process of combining multiple machine learning models to improve the accuracy and robustness of predictions.
  8. Feature engineering – The procedure of choosing and modifying features (such as variables) within a dataset to enhance the efficiency of a machine learning model.
  9. Hyperparameter tuning – The process of optimizing the settings (i.e., hyperparameters) of a machine learning model to achieve the best performance on a given dataset.
  10. Inference – The procedure of employing a trained machine learning model to generate forecasts on fresh data.
  11. ML pipeline – A series of steps that are used to build, train, and deploy a machine learning model.
  12. Model deployment – The process of making a trained machine learning model available for use by other applications or services.
  13. Model training – The process of training a machine learning model on a dataset to learn the underlying patterns in the data.
  14. Overfitting – A situation in which a machine learning model performs well on the training data but poorly on new, unseen data.
  15. Reinforcement learning – A form of machine learning that includes instructing a model to make decisions by considering feedback from its surroundings.
  16. SageMaker – A fully-managed machine learning service provided by AWS that allows developers to build, train, and deploy machine learning models at scale.
  17. Supervised learning – A machine learning category that encompasses instructing a model using labeled data, which means data that has already been categorized or classified.
  18. Unsupervised learning – A type of machine learning that involves training a model on unlabeled data (i.e., data that has not been categorized).

Exam preparation resources for the AWS Machine Learning Specialty exam

here are some official resources for AWS Machine Learning Specialty exam preparation:

  1. AWS Machine Learning Specialty Exam Guide: This guide provides an overview of the exam, its format, and what to expect. It also includes a list of recommended AWS services, whitepapers, and other resources for exam preparation. You can find the guide here: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS-Certified-Machine-Learning-Specialty_Exam-Guide.pdf
  2. AWS Certified Machine Learning Specialty Learning Path: This learning path on the AWS website provides free training resources for the exam. It includes video courses, hands-on labs, and other resources to help you prepare for the exam. You can find the learning path here: https://aws.amazon.com/training/learning-paths/machine-learning/
  3. AWS Whitepapers: AWS offers a number of whitepapers on machine learning that can be useful for exam preparation. These include “Introduction to Machine Learning on AWS”, “Building Machine Learning Pipelines on AWS”, and “Amazon SageMaker Technical Whitepaper”. You can find the whitepapers here: https://aws.amazon.com/whitepapers/
  4. AWS Sample Exam Questions: AWS offers a set of sample exam questions to help you prepare for the exam. These questions are designed to give you an idea of the types of questions you can expect on the actual exam. You can find the sample questions here: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS-Certified-Machine-Learning-Specialty_Sample-Questions.pdf
  5. AWS Machine Learning Specialty Exam Readiness: This course on the AWS website provides an overview of the exam and tips for exam preparation. It includes a practice exam to help you assess your readiness for the actual exam. You can find the course here: https://aws.amazon.com/training/course-descriptions/machine-learning-specialty-exam-readiness/

Cheat Sheet : AWS Machine Learning Specialty 

All you need to get started on your revisions is the AWS Machine Learning Specialty Cheat Sheet. It will provide you a brief overview of all the materials you’ll need to pass the test. It will also serve as your golden ticket to obtaining your certificate.

AWS Machine Learning Specialty Cheat Sheet

1. Familiarise with Exam Objectives

The first step is to gather all test regulations and course information. Before you begin your test preparations, you should familiarise yourself with the exam course. The course outline serves as the exam’s template. It goes through all of the crucial test elements and ideas that will be addressed on the exam. As a result, in order to pass the exam, you must consult the Exam Guide. The following domains are covered in this AWS Machine Learning Certification Course:

AWS Machine Learning Specialty Exam Course outline
Domain 1: Data Engineering

1.1 Create data repositories for machine learning.

1.2 Identify and implement a data ingestion solution.

1.3 Identify and implement a data transformation solution.

Domain 2: Exploratory Data Analysis

2.1 Sanitize and prepare data for modeling.

2.2 Perform feature engineering.

2.3 Analyze and visualize data for machine learning.

Domain 3: Modeling

3.1 Frame business problems as machine learning problems.

3.2 Select the appropriate model(s) for a given machine learning problem.

3.3 Train machine learning models.

3.4 Perform hyperparameter optimization.

  • Regularization (AWS Documentation:Training Parameters)
    • Drop out
    • L1/L2
  • Cross validation (AWS Documentation: Cross-Validation)
  • Model initialization
  • Neural network architecture (layers/nodes), learning rate, activation functions
  • Tree-based models (# of trees, # of levels)
  • Linear models (learning rate)

3.5 Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance. (AWS Documentation: Review the ML Model’s Predictive Performance, Best practices, Resilience in Amazon SageMaker)

4.2 Recommend and implement the appropriate machine learning services and features for a given problem.

4.3 Apply basic AWS security practices to machine learning solutions.

4.4 Deploy and operationalize machine learning solutions.

2. Know about the Learning Resources

There are several tools available to help you prepare for the test. However, you must determine which are useful to you. The resources allow you to achieve more in less time. Here you will find easy connections to all of the resources you will require to pass the exam.

– AWS Machine Learning White Paper

The AWS team offers several whitepapers aimed at enhancing your technical expertise. These whitepapers are developed exclusively by the AWS team, analysts, and other AWS collaborators. You may wish to focus your attention on the following whitepapers:

– Online Training Courses

AWS Machine Learning Certification Training is accessible in a variety of formats. You may find the training programs that are most appropriate for you based on the curriculum and your time available. There are both online and instructor-led classes available, both of which provide an interactive learning environment. Additionally, you may clarify your concerns and take the test series along with the courses from the same site. For more training options, you can visit Training Library by Amazon for machine learning.

– Recommended Progression
– Branching content areas
– Optional training

3. Reference Books

The greatest valuable resource of all time is booked. For the AWS machine learning specialty test, you can consult a number of resources. You can select any book that covers all areas of the curriculum and is written in a language that is comfortable for you. There are several books available, including:

  1. Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow
  2. Effective Amazon Machine Learning
  3. Learning Amazon Web Services (AWS): A Hands-On Guide to the Fundamentals of AWS Cloud | First Edition | By Pearson

4. Online Tutorials and Study Guide

Online Tutorials help you improve your knowledge and have a better comprehension of test themes. Exam specifics and policies are also covered in the AWS Machine Learning Tutorials. As a consequence, learning using Online Tutorials will help you improve your preparedness. Furthermore, Study Guides will be a valuable resource for you as you prepare for the AWS Machine Learning Specialty test. These resources will assist you in remaining consistent and determined.

AWS Machine Learning Specialty Online Tutorial

5. Attempt Practice Tests

The only method to pass the test with a good score is to take the AWS Machine Learning Practice Exam. Your concepts will become more apparent as you practise. Always practise sample papers and take as many exam series as possible. This will aid in the discovery of your flaws and the identification of your weak spots. Furthermore, you will discover the areas where you need to improve and the areas where you are completely prepared for the exam. This is the most crucial aspect of the preparatory process. Many reputable educational websites provide example papers with a 100% guarantee of achievement. Try a free practice test now!

AWS Machine Learning Specialty Free Practice Tests
Upgrade your skills and get ready to qualify the AWS Machine Learning Specialty Exam with latest practice tests and expert learning resources. Start Preparing Now!
Menu