Is it difficult to pass the AWS Machine Learning Specialty Exam Blog

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Is it difficult to pass the AWS Machine Learning Specialty Exam Blog

Computer geeks are increasingly interested in machine learning. The AWS Machine Learning specialist exam covers Amazon Web Services products that allow developers to apply algorithms to uncover patterns and build mathematical models based upon these patterns. These models can then be used to create and execute predictive apps. Every company wants its most valuable asset, its workforce, to be technologically current at all times. It is crucial to stay on top of technological developments if you work in an IT company.
Let’s learn more about the AWS Machine Learning Specialty Exam
About the AWS Machine Learning Specialty Exam
AWS now offers more advanced and professional certifications that are based on the latest technologies. This will allow you to improve your skills and knowledge to make a career out of AWS. AWS Machine Learning Specialty certification was created for data scientists and developers. The certification exam also measures your ability train, tweak, deploy and develop machine learning (ML), models using AWS cloud.
As more high-level jobs become available, the demand for AWS Machine Learning Specialty certificates is increasing. This certification is the best way to put your knowledge, expertise, time, and effort to work to earn more and better reputation. Make sure you have all the necessary materials in one place before you begin preparing for the test.
Exam Prerequisites
Preparation for the AWS Machine Learning Specialty exam
You should have at least 1 to 2 years experience in developing, architecting, running, and maintaining machine learning and deep learning workloads on AWS.
Additionally, you should be familiar with machine and deep learning frameworks as well as hyper-parameter optimizations.
It is also important to follow operational, deployment, as well as model training best practices.
AWS Machine Learning Specialty Exam Details
Multiple-choice questions are part of the AWS Machine Learning Certification Exam. You have 170 minutes to complete the exam. AWS Machine Learning Certification costs USD 300. The test can be accessed in English, Japanese and Korean, as well as Simplified Chinese.
Exam CodeMLS-C01Exam TypeSpecialtyExam Duration170 minutesExam CostUSD 300Exam FormatMultiple-choice Questions and Multiple-response QuestionsExam ScoringScaled score from 100 to 1000Passing Score750Exam LanguageEnglish, Japanese, Korean, and Simplified Chinese Let us now move on to the course structure of this exam for more clarity.
Exam Course Outline
This AWS Machine Learning Specialty Certificate can be focused on 4 domains
Domain 1: Data Engineering
First, create data repositories to support machine learning. This module is described in Amazon documentation: Use Amazon S3 as a repository for data, Amazon Redshift as a source of data, Amazon RDS Database to access Amazon ML Datasources.
Secondly, you must identify and implement a data ingestion solution. (AWS Documentation – Data Ingestion Methods in AWS. Learn how data is ingested using Amazon SageMaker and a Data Lake. How Kinect Energy ingests information to forecast energy prices.
Third, identify and implement a data transformation solution. (AWS Documentation:N-gram Transformation,Orthogonal Sparse Bigram (OSB) Transformation,Lowercase Transformation,Data Rearrangement: Create datasource based on a section of the input data)
Domain 2: Exploratory Data Analysis
First, clean and prepare the data for modeling. (AWS Documentation:Prepare your data in Amazon Machine Learning,Use Amazon SageMaker Ground Truth for Data Labeling,Prepare data in Amazon SageMaker)
Secondly, perform feature engineering. (AWS Documentation:Understanding the Importance of Feature Transformation,Feature Processing in Amazon Machine Learning,Feature Processing using Spark & Scikit-learn in SageMaker)
Finally, analyze and visualize data to support machine learning. (AWS Documentation:Analyzing Data with Amazon Machine Learning,Explore, Analyze & Process data,Visualizing the distribution of data,Visualizing insights for binary models,Visualizing insights for Regression models)
Domain 3: Modelling
First, consider business problems as machine-learning problems. (AWS Documentation:Resources from AWS: Formulating the Problem,Resources from Amazon: Solving Business Problems with Amazon ML)
Next, choose the right model(s) to solve the given machine learning problem. (AWS Documentation.Amazon Machine Learning: Types and ML Models).
You can also train machine learning models. (AWS Documentation:Build, Train, and Deploy a Machine Learning Model with SageMaker,Train a Model with Amazon SageMaker,Incremental training of model in SageMaker,Training with Amazon EC2 Spot Instances,Train a Deep Learning model)
Also, optimize hyperparameters. (AWS Documentation:Understanding the Training Parameters,Hyperparameters available in Amazon ML,How does Hyperparameter Tuning work?,Defining Hyperparameter Ranges,Best Practices for Hyperparameter Tuning)
Final, evaluate machine learning models. (AWS Documentation – Binary Model Insights and Multiclass Model Insights. Regression Model Insight. Understand the Cross-validation technique to evaluate ML Models. Evaluating Model Fit: Overfitting vs. Underfitting.
Domain 4: Machine Learning Implementation & Operations
First, create machine learning solutions that improve performance, availability, scalability and fault tolerance. (AWS Documentation):Review the ML Model’s Predictive performance, Deploy Multiple Instances Across Availability Zones. Amazon SageMaker: Infinitely Scalable Machine Learning Methodms. Read this Whitepaper: Power Machine Learning on Scale.
Then, recommend and implement appropriate machine learning features and services for a give.