How to Prepare for AWS Certified Machine Learning Specialty?

AWS offers different certification programs to recognize talents in different fields. In the field of Data Science and Machine Learning, it offers two popular certifications – AWS Certified Data Analytics – Specialty and AWS Certified Machine Learning – Specialty. In the previous article, we discussed the AWS Certified Data Analytics – Specialty that how one can prepare and crack the exam to achieve this certification. In this article, we will discuss in detail the AWS Certified Machine Learning Specialty certification and how to prepare for it.

AWS Certified Machine Learning – Specialty

This is a suitable certification for those professionals who are working in the field of machine learning and looking for recognition to stand out. This certification not only certifies the machine learning skills of a professional but also highlights the knowledge and understanding of different AWS cloud services related to machine learning and deep learning. Through this certification, AWS aims to assess and give recognition to the professionals for their expertise in designing, implementing, deploying and maintaining machine learning solutions for given business problems. Along with that, the candidate’s ability is also checked regarding identifying appropriate AWS services to implement machine learning solutions and designing and implementing scalable, cost-optimized, reliable, and secure machine learning solutions.


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AWS recommends this certification to the candidates with 1-2 years of experience in developing, architecting and running machine learning and deep learning workloads on the AWS Cloud platform. It expects that the candidates must have the ability to express the intuition behind basic machine learning algorithms. Along with that, they must be able to perform basic hyperparameter optimization while working with different machine learning and deep learning frameworks followed by best practices in training, deployment and operationalization of models. 

About AWS Certified Machine Learning Specialty Exam

To achieve AWS Certified Machine Learning Specialty certification, candidates need to pass an exam conducted by AWS. This exam can be taken from a test centre or online from anywhere with online proctoring. To appear for this exam, the candidates need to register themselves by paying a registration fee of $300 USD.

The important details about the exam are given below.

  • Duration: 3 Hours
  • Number of questions: 65
  • Format: Multiple-Choice questions with one correct option or more than one correct option.

The exam is evaluated on a scale of 100 to 1000 where the candidates need to obtain a minimum of 750 to pass the exam. The questions in the exam appear from different domains of machine learning. These domains or sections with their weightage are given below.

  1. Data Engineering(20%)
  2. Exploratory Data Analysis (24%)
  3. Modelling (36%)
  4. Machine Learning Implementation and Operations (20%)

However, there is no sectional cutoff of passing marks, you will get the result with your performance in each of the above sections. The main result is based on the overall performance in the exam.

How to Prepare?

Good preparation is the key to success. As the concepts to be covered for this exam are a little advanced, there must be proper planning to crack this exam. Here are the important steps that a candidate planning to take this exam should follow to prepare.

  1. Collecting the Relevant Information: You must collect all the important information related to this exam yourself rather than only believing in the blogs on the internet. You can find all the highlights of the certification and exam on its official website. The complete guide to the exam with the content to be covered is provided by AWS. You can find it through this link. AWS has also given a set of sample questions that can be found using this link. You should go through the exam guide and sample questions to understand the breadth and depth of the exam.
  1. Learning through AWS Resources: You can find hundreds of tutorials related to different AWS services and use cases that will help you to gain and brush up on the required skills. These tutorials can be found using this link. You can check the learning resources based on the domains as discussed above. Along with this, it is also suggested to go through the descriptions and use cases of all the relevant AWS cloud products. You can check this link to find a catalogue of all the AWS cloud products. 
  1. Mock Test on MachineHack: MachineHack, India’s leading hackathon and assessment platform, offers a variety of mock tests on similar kinds of professional certification exams. It also offers the mock test on AWS Certified Machine Learning Specialty Exam. Through this mock, you can get an experience of taking this exam in real life as the mock is based on exam details as given by AWS and feedback from the successful candidates. This mock will help you in self-assessment of your preparedness for this exam.

After taking a mock on MachineHack, candidates will be able to assess their knowledge and level of preparation. If they score well in mocks, they can go ahead and appear for the exam. Otherwise, they can again improve their knowledge with AWS learning resources followed by a further mock.

Final Words

Although the AWS Certified Machine Learning Specialty exam is a little bit difficult to crack as it requires in-depth knowledge and understanding of different machine learning and deep learning workloads to be defined and used with AWS Cloud services, it will not be as difficult if you have a right plan for preparation. As discussed above, you can go through a variety of learning resources to gain and brush up your knowledge followed by taking the mock test on the MachineHack platform. Mock will help you in the self-assessment of knowledge and preparations for the exam. If you follow the steps as we discussed above, you can crack the exam easily with a result as certification in hand.

Best of luck for the exam!

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Dr. Vaibhav Kumar
Vaibhav Kumar has experience in the field of Data Science and Machine Learning, including research and development. He holds a PhD degree in which he has worked in the area of Deep Learning for Stock Market Prediction. He has published/presented more than 15 research papers in international journals and conferences. He has an interest in writing articles related to data science, machine learning and artificial intelligence.

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