For IT enthusiasts, machine learning is a hot trend. AWS Machine Learning Specialty Certification, an Amazon Web Services product, allows a developer discover patterns in end user data using algorithms. Then, he or she can create mathematical models based upon these patterns and then create and execute predictive applications. Every organization wants its most valuable asset, its workforce, to be constantly updated in the technology domain. If you work for an IT company, it is important to keep yourself up-to-date on the technological side. These certifications show your dedication to your work and your company. You will feel more confident and stand out from the rest of the crowd if you keep yourself up to date.
Amazon’s AWS machine-learning specialty was voted the most difficult among all certifications. These types of IT certifications are difficult to crack and require knowledge of the subject. It is not enough to just get the certificate. To fully understand the subject, you must have a solid understanding of it. If you have the right resources and a plan, all this can be accomplished. This is the place to go if you want to pass the AWS machine-learning specialty exam.
Continue reading until the end to get all the information you need. Let us get underway!
What is the Amazon machine learning specialty exam?
Individuals who work in data science or development are eligible to take the AWS Machine Learning Certification exam (MLS-C01). This exam validates the examinee’s ability build, train, tune and deploy machine learning (ML), models using AWS Cloud.
It assesses the candidate’s ability to implement, maintain, and manage ML solutions for business problems. It will confirm the candidate’s ability:
Choose and justify the best ML approach to solve a business problem.
Identify the appropriate AWS services for implementing ML solutions.
Implement scalable, cost-optimized and reliable ML solutions that are both secure and scalable.
The exam will assess your knowledge of the following domains. Each domain’s weightage is also included.
Data Engineering – 20%
Secondly, Exploratory Data Analysis 24%
Thirdly, Modeling – 36%
Final, Machine Learning Implementation & Operations – 20 %
Let’s take a look at the AWS machine-learning specialty exam overview to get a better understanding of it.
Exam overview
The AWS Machine Learning Specialist Certification exam has 65 questions that are scenario-based. These questions are used to assess a candidate’s ability solve different business problems. The exam lasts 170 minutes and is a specialty exam. The AWS machine learning specialty exam costs $300, although prices can vary from one place to another. Pearson VUE can schedule the exam. Multiple choice questions and multiple answer questions are the types of questions that will be asked. AWS machine learning specialty exam is scored on a scale from 1 to 1000. Passing score is 750 marks. AWS machine learning specialty exam can be taken in English, Japanese and Korean.
Here are the details:
Name of the examAWS machine learning specialtyExam codeMLS-C01Exam typeSpecialtyExam duration170 minutesExam cost$300FormatMultiple choice questions and multiple response questionsPassing score750 marksLanguages availableEnglish, Japanese, Korean, and Simplified ChineseNow let us have a look at the knowledge and experience recommended by amazon for taking this exam.
AWS Machine Learning Certification Prerequisites
Amazon recommends that candidates appearing for the AWS machine-learning specialty exam have the following knowledge and experience
First, 1-2 years experience in developing, architecting or running ML/deep-learning workloads on AWS Cloud.
The ability to express intuition behind basic ML algorithms is then gained.
Also, experience in basic hyperparameter optimization.
Additionally, experience with deep learning frameworks and ML.
The ability to follow best practices in model-training.
Finally, the ability follow operational best practices and deployment.
Let’s now move on to the most important part: the syllabus for AWS machine-learning specialty.
Syllabus outline
These domains will be tested in the Amazon AWS Machine learning Certification exam exam. The composition of the domains is also fixed. Let’s take a look!
Domain 1: Data Engineering
First, create data repositories to support machine learning.
Secondly, you must identify and implement a data-ingestion system.
Third, find and implement a data transformation solution.
Domain 2: Exploratory Data Analysis
First, clean and prepare data for modeling.
Secondly, perform feature engineering.
Finally, analyze and visualize data to aid in machine learning.
Domain 3: Modelling
First, think of business problems as machine-learning problems.
Next, choose the best model for the given machine learning problem.
You can also train machine learning models.
Also, optimize hyperparameters.
Final, evaluate machine learning models.
Domain 4: Machine Learning Implementation & Operations
First, create machine learning solutions that improve performance, availability, scaleability, resiliency and fault tolerance.
The next step is to recommend and implement appropriate machine learning features and services for a particular problem.
You should also follow the AWS security guidelines to ensure machine learning solutions are protected.
Finally, deploy and operate machine learning solutions.
We now have all the details about the AWS machine-learning specialty exam, let’s move on to the preparation materials for the exam.
Preparatory materials for the AWS machine learning specialty exam
AWS Machine Lear