How to prepare for AWS machine learning specialty exam (MLS-C01) ?

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The AWS Certified Machine Learning – Specialty (MLS-C01) exam is a test from Amazon Web Services (AWS) that checks if you can create, set up, put into action, and manage machine learning (ML) solutions on AWS. It’s for people who know a lot about ML and have experience using AWS services for ML projects.

The exam covers a broad range of topics related to ML on AWS, including data engineering, exploratory data analysis, modeling, machine learning algorithms, AWS services for ML, model deployment and monitoring, and business and ethical considerations.

To prepare for the exam, AWS recommends that you have at least one year of hands-on experience with machine learning and experience using AWS services for ML workloads. You can also take advantage of the many training and certification resources offered by AWS, including instructor-led courses, online training, and self-paced labs.

Getting the AWS Certified Machine Learning – Specialty certification shows that you’re really good at making, using, and taking care of ML solutions on AWS. This can open up new job chances and help you move ahead in the ML field.

AWS machine learning specialty (MLS-C01) Glossary

  • Machine Learning (ML): A part of artificial intelligence (AI) where it teaches computer programs to make guesses or choices using information from data.
  • Supervised Learning: A kind of machine learning method where a model learns from data that’s already labeled to make predictions on new, unlabeled data.
  • Unsupervised Learning: A type of ML algorithm that involves training a model on unlabeled data to identify patterns and structures in the data.
  • Reinforcement Learning: A type of ML algorithm that involves training a model to make decisions based on feedback received from its environment.
  • Deep Learning: A subset of ML that involves training deep neural networks with many layers to make predictions or decisions.
  • Data Engineering: The process of collecting, preparing, and transforming data for use in ML models.
  • Exploratory Data Analysis (EDA): The process of visualizing and summarizing data to gain insights and identify patterns.
  • Model Selection: The process of choosing the best model for a particular ML problem based on its performance on a validation dataset.
  • Learning Model Deployment: The process of making an ML model available for use in a production environment.
  • Model Monitoring: The process of monitoring the performance of an ML model over time and making updates as necessary.
  • AWS Services for ML: A suite of AWS services that enable users to build, train, deploy, and monitor ML models, including Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

AWS machine learning specialty (MLS-C01) Study Guide

  1. Exam Guide: The AWS Certified Machine Learning – Specialty Exam Guide provides a detailed overview of the topics covered on the exam, the format of the exam, and the passing score. You can access the guide here: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS-Certified-Machine-Learning-Specialty_Exam-Guide_EN.pdf
  2. Sample Questions: AWS provides a set of sample exam questions to help you prepare for the exam. The questions are intended to give you a better understanding of the types of questions you can expect to see on the exam. You can access the sample questions here: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS-Certified-Machine-Learning-Specialty_Sample-Questions.pdf
  3. Exam Readiness Course: The AWS Exam Readiness course for the AWS Certified Machine Learning – Specialty exam is designed to help you prepare for the exam. The course covers exam structure and question formats, tips for answering exam questions, and sample questions with explanations. You can access the course here: https://www.aws.training/Details/eLearning?id=42143
  4. AWS Whitepapers: AWS provides a number of whitepapers that are relevant to the AWS Certified Machine Learning – Specialty exam. These include the AWS Well-Architected Framework, the AWS Machine Learning Lens, and the AWS Security Best Practices. You can access these whitepapers here: https://aws.amazon.com/whitepapers/
  5. AWS Certified Machine Learning – Specialty Certification: The AWS Certified Machine Learning – Specialty certification page provides information about the exam, including prerequisites, recommended knowledge and experience, and certification benefits. You can access the certification page here: https://aws.amazon.com/certification/certified-machine-learning-specialty/

AWS machine learning specialty (MLS-C01) Exam tips and tricks

  • Understand the Exam Format: The exam consists of 65 multiple-choice and multiple-response questions and you have 180 minutes to complete it. It’s important to understand the exam format so you can manage your time effectively and not get bogged down on any one question.
  • Review AWS Services: The exam covers a wide range of AWS services related to machine learning, so it’s important to be familiar with them. Review services like Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, and Amazon Elastic Inference.
  • Practice with Sample Questions: AWS provides a set of sample exam questions that you can use to prepare for the exam. Practice answering these questions to get a better sense of the types of questions you’ll see on the exam.
  • Study Key Concepts: Ensure you grasp important machine learning ideas like supervised learning, unsupervised learning, and reinforcement learning. Also, go over data engineering concepts such as getting data ready, changing data, and exploring data.
  • Review AWS Best Practices: Review AWS best practices related to machine learning, such as the AWS Well-Architected Framework, the AWS Machine Learning Lens, and the AWS Security Best Practices. These can help you understand how to build and deploy machine learning models in a secure and reliable way.
  • Use AWS Documentation: Use the official AWS documentation to deepen your understanding of the services and concepts covered on the exam. AWS provides detailed documentation on all of its services, which can be a valuable resource in your exam preparation.
  • Join a Study Group: Consider joining a study group to connect with other professionals preparing for the same exam. You can discuss key concepts, review sample questions, and provide support to each other as you prepare for the exam.

Syllabus outline

The Amazon AWS Machine Learning Certification exam  exam will test you on the basis of following domains. The compositions of the domains are also fixed. Let us have a look

Domain 1: Data Engineering (20%)

1.1 Create data repositories for ML.

1.2 Identify and implement a data ingestion solution.

1.3 Identify and implement a data transformation solution.

Domain 2: Exploratory Data Analysis (24%)

2.1 Sanitize and prepare data for modeling.

2.2 Perform feature engineering.

2.3 Analyze and visualize data for ML.

Domain 3: Modeling (36%)

3.1 Frame business problems as ML problems.

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

3.3 Train ML models.

3.4 Perform hyperparameter optimization.

  • Perform Regularization (AWS Documentation:Training Parameters)
    • Drop out
    • L1/L2
  • Perform Cross validation (AWS Documentation: Cross-Validation)
  • Model initialization
  • Neural network architecture (layers/nodes), learning rate, activation functions
  • Understand tree-based models (number of trees, number of levels).
  • Understand linear models (learning rate).

3.5 Evaluate ML models.

Domain 4: Machine Learning Implementation and Operations (20%)

4.1 Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance. (AWS Documentation: Review the ML Model’s Predictive PerformanceBest practicesResilience in Amazon SageMaker)

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

4.3 Apply basic AWS security practices to ML solutions.

4.4 Deploy and operationalize ML solutions.

Now that we have every detail about the AWS machine learning specialty exam let us move to the preparatory resources for the exam.

Preparatory resources for AWS machine learning specialty exam

AWS Machine Learning Specialty Preparations  are quite challenging one and requires a lot of dedication and hard work combined with right set of resources to ace the exam. There are numerous resources but we need to figure out the ones which are beneficial for us. The resources through which we can gain more in less time. This will give you more time for practicing and reviewing. Now, let’s explore some helpful resources that will assist you in acing the exam.

Resource 1: the official site

The official site of amazon recommends the hands-on experience along with the online training and sample papers in order to ace the exam. Always make sure to visit the official site to gather details about every detail of the exam. The official site provides knowledge about technical aspects about the exam and about the latest updates of the exam. There are many official resources that are made available the amazon for the exam. Amazon is also providing free webinars to help spread knowledge about the exam.

Resource 2: online training programs

There are many  AWS Machine Learning Certification Training programs which are made available by the educational sites. You can find the training programs that are best suitable to you according to the syllabus and availability of time. There are online classes as well as instructor-led classes which offers interactive way of learning. You can clear your doubts without any hesitation and take the test series along with the courses from the same site.

Resource 3: books

Books are the most valued resources for all time. You can refer to many books for AWS machine learning specialty exam.  You can choose any book that covers the aspects of the syllabus and has the language according to your ease. There are many books available as:

Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow

Machine Learning with Aws

Effective Amazon Machine Learning

Learning Amazon Web Services (AWS): A Hands-On Guide to the Fundamentals of AWS Cloud | First Edition | By Pearson

Pragmatic AI an Introduction to Cloud Based Machine Learning

Resource 4: join study groups and discussions

You can join many study groups for improving your preparations and pooling different resources. Discussions help you test your knowledge. Try to form the groups with the people who are more interactive as this will help you in getting answers quickly. This will help to instill a competitive spirit in you and increase your performance.

Resource 5: practice papers and test series

The AWS Machine Learning Practice Exam is the best method to succeed in the test with a high score. The more you practice, the better you’ll understand the concepts. Regularly practice sample questions and take practice tests as much as possible. This will help you discover where you’re struggling and where you need improvement. It will also show you which areas you’re well-prepared for in terms of the exam. This is a crucial part of getting ready. Numerous trusted educational websites provide sample questions and ensure a 100% success rate. Try a free practice test now!

This was the list of some of the resources that you can use for preparation. Now let us move to the conclusion part along with some of the tips.

Conclusion

AWS Machine Learning Certification Difficulty is really high as compared to other AWS certifications. You need to be completely focused in order to pass the exam. Make sure to revise the important concepts on the exam day and follow your schedule strictly. Make sure to make notes so that you do not miss out on anything important. And practice as much as you can.

You will surely get the certification and make yourself proud. Just a pinch of hard work, a pinch of dedication mixed with right set of resources is required to clear the exam and showcase your abilities.

All the best!

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