AWS Machine Learning is a specialty-level credential from AWS that involves machine learning solutions and fundamentals to solve business challenges. It covers the three-tier structure AWS uses to categorize its certification i.e., Foundational, Associate, and Professional.
Acquiring AWS machine learning certification not just gives a solid grasp of the foundation of ML but also provides viable solutions through the AWS cloud. It is designed for people who perform a development or data science role and have over a year of experience in developing or running machine learning in the AWS cloud. Before you appear for this exam, we suggest you have:
- At least two-year experience in developing, architecting, and operating deep ML workloads in AWS cloud
- Specialized in performing basic hyperparameter optimization
- Experience in ML and related frameworks
- Capability to track model training, deployment, and operational practices
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Overview of AWS Machine Learning – Specialty
This is a thriving opportunity for individuals performing data development roles. It encourages the ability to build, design, deploy and implement ML solutions depending on the organization’s requirements.
It is no denying that AWS or Amazon Web Services has been a widely adopted cloud platform since 2006. It offers future-centric services for networking, storage, analytics, database, developer, management, deployment, IoT, enterprise applications, AI, and security.
This certification is ideal for those who work in the sphere of machine learning and looking for recognition to gain a competitive edge. It gives an individual a thorough understanding of various AWS cloud services associated with machine learning and deep learning.
AWS Certified Machine Learning Specialty Exam
To become AWS certified individual, you need to clear AWS machine learning specialty exam conducted by AWS. The key highlights of the exam are given below:
- Duration – 3 Hours
- Number of Questions – 65
- Format – Multiple-Choice questions
The exam is evaluated on a scale of 100 to 1000 where applicants need to attain a minimum of 750 to clear the exam. The questions will appear from different domains of machine learning, including data engineering, exploratory data analytics, modeling, and machine learning implementation and operations. The main result is based on the performance in each of these sections.
How to Prepare for AWS ML Specialty Exam?
Relevant preparation and study plan is key to success. As the concepts covered are a bit advanced, you will require proper planning and time management to pass the exam. Follow the below steps to prepare for the AWS ML specialty exam:
- Complete all the learning content
- Read exam guide
- Take Qwikskills preparation exams many times
- Examine AWS whitepapers for additional information
- Schedule your exam
Qwikskills is a trusted platform designed for candidates to know their strengths and weaknesses before appearing for the real exam. With the help of practice tests, learners can get access to all cloud certification programs, such as GCP associate cloud engineer, and obtain knowledge of actual exam-related questions.
Cracking the AWS ML specialty exam isn’t as difficult as it seems if you will study from relevant learning resources and practice tests to sharpen your knowledge.
Also Read: Tips To Prepare for the IELTS Test at Home
