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AWS Certified Machine Learning – Specialty is a standard exam for candidates who want to excel in the development field and data science and verify their competence by earning certification. This test, coded MLS-C01, helps the individuals to measure their knowledge of the design, deployment, implementation, and maintenance of machine learning solutions.
This exam allows candidates to validate their skills related to choosing the right AWS services for Machine learning implementation and resolving business problems. Also, they prove their ability to create reliable, cost effective, and secure ML solutions.
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:
- 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.
- 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.
Amazon MLS-C01日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning Implementation and Operations | 20% | - CI/CD for ML workflows - ML pipeline deployment on AWS - Amazon SageMaker usage and operations - Model monitoring and maintenance |
| Topic 2: Modeling | 36% | - Hyperparameter tuning - Supervised and unsupervised learning - Deep learning concepts - Algorithm selection and evaluation |
| Topic 3: Data Engineering | 20% | - Feature engineering pipelines - Data preprocessing and transformation - Data storage and data ingestion |
| Topic 4: Exploratory Data Analysis (EDA) | 24% | - Data visualization and statistical analysis - Data quality assessment - Feature correlation and selection |
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