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How to Prepare For AWS Certified Machine Learning - Specialty

Preparation Guide for AWS Certified Machine Learning - Specialty

Introduction for AWS Certified Machine Learning - Specialty

The AWS Certified Machine Learning - Specialty (MLS-C01) examination is intended for individuals who perform a development or data science role. This exam validates an examinee's ability to build, train, tune, and deploy learning (ML) models using the AWS Cloud.

It validates an examinee's ability to design, implement, deploy, and maintain ML solutions for given business problems. It will validate the candidate's ability to:

• Select and justify the appropriate ML approach for a given business problem.
• Design and implement scalable, cost-optimized, reliable, and secure ML solutions
• Identify appropriate AWS services to implement ML solutions.

The AWS Certified Machine Learning - Specialty certification is intended for individuals who perform a development or data science role. It validates a candidate's ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

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Exam Topics

As for the MLS-C01 certification test, there are 4 domains that are presented in the exam content. All in all, the topics you need to focus on when preparing for this test are highlighted below:

• Data Engineering

  This domain makes up about 20% of the total questions in the entire exam content. It measures the test takers’ ability to accomplish the technical tasks such as creating machine and learning data repositories. Another section that is included in this topic is implementing & identifying the data ingestion solution. Besides that, the students are also required to demonstrate their skills in implementing and identifying data transformation solutions.

• Modeling

  This is the most prominent part of this exam with a weightage of 36%. This means that it is impossible to get the passing score if you have not mastered this topic. It covers the following sections, including framing business problems as machine learning problems and training machine learning model. In addition, the test takers need to be ready to demonstrate their competence in selecting an appropriate model for specific machine learning problems. They have to possess the ability to perform hyperparameter optimization as well as perform the task of evaluating machine learning models.

• Exploratory Data Analysis

  The second subject area covers about 24% of the total weightage and requires that the learners know about data for machine learning. They should know more about machine modeling concepts as well. The questions in this section demand that the candidates possess the skills in preparing data & sanitizing that particular data for modeling. Other items seek to verify their expertise in performing feature engineering. Last but not least, the applicants should demonstrate their understanding of how to perform visualization and canalization of data for machine learning.

• Machine Learning Implementation and Operations

  This is the last objective of the certification exam. It mainly includes the concepts of machine learning services as well as implementation. It carries about 20% weightage in the test. One of the most prominent sections covered within this domain is building machine learning solutions for availability, fault tolerance, resiliency, and performance. It also evaluates the candidates’ knowledge and skills in implementing and recommending the correct machine learning services particularly for a given problem. In addition, the examinees should be ready to demonstrate that they know about applying basic Amazon Web Services (AWS) security practices to specific machine learning solutions. Also, this part requires that the IT pros attempting this test make evident that they have the skills and knowledge required to deploy and operationalize machine learning solutions.

What Topics Are Covered in AWS Machine Learning - Specialty Certification Exam?

The certification exam for the AWS Machine Learning – Specialty certification tests the candidates' ability to select the best machine learning strategy to improve the business processes. Also, they will be able to identify the best AWS service needed to implement different machine learning solutions. Besides, candidates will be able to design and put into practice reliable, scalable, and cost-optimized machine learning solutions. The AWS MLS-C01 exam, in particular, focuses on four domains. They are the following:

• Exploratory Data Analysis;
• Data engineering;
• Machine Learning Implementation and Operations.
• Modeling;

The first topic handles data engineering and has 3 sections. The first one handles the creation of data repositories for efficient machine learning strategies. Also, candidates will learn how to effectively identify and implement solutions related to data-ingestion. The third sub-domain included in this section focuses on the implementation and identification of different data-transformation solutions.

The second tested area concentrates on exploratory data analysis. When they prepare for this topic, candidates will learn how to sanitize and prepare data for modeling. Also, they will learn how to make a performance when it comes to feature engineering. Finally, candidates will learn how to visualize data and analyze different parameters for machine learning.

Within the modeling section, candidates will learn how to frame different business problems related to machine learning issues. Besides, they will find how to select the right models for different machine learning problems. In this section, candidates will also learn how to train effectively for machine learning models. Another essential concept that candidates will discover in this part is related to hyperparameter optimization performance. Last but not least, applicants will understand how to correctly evaluate machine learning models.

The final domain concentrates on machine learning operations and implementation. This segment focuses on helping candidates develop advanced abilities in building machine learning solutions to achieve the highest performance and fault tolerance. These solutions will help them ensure availability, resilience, and scalability. Another subtopic included in the last objective deals with the recommendations and implementation of the right machine learning services adapted to the business context. Candidates will also learn how to apply fundamental AWS security practices to solve different machine learning issues. Finally, they will become ready to deploy and operationalize various machine learning solutions.

What are the exam results for AWS Certified Machine Learning - Specialty

The examination is a pass or fail exam. The examination is scored against a minimum standard established by AWS professionals who are guided by certification industry best practices and guidelines. Your results for the examination are reported as a score from 100-1,000, with a minimum passing score of 720. Your score shows how you performed on the examination as a whole and whether or not you passed. Scaled scoring models are used to equate scores across multiple exam forms that may have slightly different difficulty levels. Your score report contains a table of classifications of your performance at each section level. This information is designed to provide general feedback concerning your examination performance. The examination uses a compensatory scoring model, which means that you do not need to “pass” the individual sections, only the overall examination. Each section of the examination has a specific weighting, so some sections have more questions than others.

Reference: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS%20Certified%20Machine%20Learning%20-%20Specialty_Exam%20Guide%20(1).pdf