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The course covers data preparation, ML model development, deployment and orchestration, plus monitoring, maintenance, and security, including prerequisites and recommended AWS experience.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/exam-prep-aws-certified-machine-learning-engineer-associate-mla-c01-course-code-910030/317517/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/exam-prep-aws-certified-machine-learning-engineer-associate-mla-c01-course-code-910030/317517.png","ImageObject",300,407,{"name":92,"@type":93},"Aria Callaghan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-20",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",17,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What does the AWS Certified Machine Learning Engineer-Associate (MLA-C01) exam validate?","Question",{"text":113,"@type":114},"It validates the ability to build, operationalize, and maintain machine learning solutions and pipelines using the AWS Cloud.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Does the course require prior training before attendance?",{"text":118,"@type":114},"No specific training is required before taking the course, but prerequisite knowledge is recommended for best preparation.",{"name":120,"@type":111,"acceptedAnswer":121},"What topics are covered in the virtual classroom live modules?",{"text":122,"@type":114},"Modules include data preparation, ML model development, deployment and orchestration of ML workflows, and monitoring, maintenance, and security for ML solutions.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},317517,1790001949,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":19,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":52,"preview_page_count":4},962084926284,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","EXAM PREP: AWS CERTIFIED MACHINE LEARNING ENGINEER  \nCourse Code: 910030  \nAWS Certified Machine Learning Engineer – Associate (MLA-C01) is a one-day ILT where you learn how to assess your preparedness for the AWS Certified Machine Learning Engineer-Associate (MLA-C01) exam. The exam validates a candidate’s ability to build, operationalize, and maintain machine learning (ML) solutions and pipelines by using the AWS Cloud.  \nThis intermediate-level course prepares you for the AWS Certified Machine Learning Engineer-Associate (MLA-C01) exam by providing a comprehensive exploration of the exam topics. You'll delve into the key areas covered on the exam, understanding how they relate to developing AI and machine learning solutions on the AWS platform. Through detailed explanations and walkthroughs of exam style questions, you'll reinforce your knowledge, identify gaps in your understanding, and gain valuable strategies for tackling questions effectively. The course includes review of exam-style sample questions, to help you recognize incorrect responses and honeyour test-taking abilities. By the end, you'll have a firm grasp on the concepts and practical applications tested on the AWS Certified Machine Learning EngineerAssociate (MLA-C01) exam.  \nWhat You'll Learn  \nIn this course, you will learn to:  \n• Identify the scope and content tested by the AWS Certified Machine Learning Engineer-Associate (MLA-C01) exam.  \n• Practice exam-style questions and evaluate your preparation strategy.  \n• Examine use cases and differentiate between them.  \nWho Needs to Attend  \nYou are not required to take any specific training before taking this course. However, the following  \n• Prerequisite knowledge is recommended prior to taking the AWS Certified Machine Learning Engineer-Associate (MLA-C01) exam.  \nPrerequisites  \nGeneral IT knowledge:  \nLearners are recommended to have the following:  \n• Suggested 1 year of experience in a related role such as a backend software developer, DevOps developer, data engineer, or data scientist.  \n• Basic understanding of common ML algorithms and their use cases  \n• Data engineering fundamentals, including knowledge of common data formats, ingestion, and transformation to work with ML data pipelines  \n• Knowledge of querying and transforming data  \n• Knowledge of software engineering best practices for modular, reusable code development, deployment, and debugging  \n• Familiarity with provisioning and monitoring cloud and on-premises ML resources  \n• Experience with continuous integration and continuous delivery (CI/CD) pipelines and infrastructure as code (IaC)  \n• Experience with code repositories for version control and CI/CD pipelines.  \nRecommended AWS knowledge:  \nLearners are recommended to be able to do the following:  \n• Suggested 1 year of experience using Amazon SageMaker AI and other AWS services for ML engineering.  \n• Knowledge of Amazon SageMaker AI capabilities and algorithms for model building and deployment  \n• Knowledge of AWS data storage and processing services for preparing data for modeling  \n• Familiarity with deploying applications and infrastructure on AWS  \n• Knowledge of monitoring tools for logging and troubleshooting ML systems  \n• Knowledge of AWS services for the automation and orchestration of CI/CD pipelines  \n• Understanding of AWS security best practices for identity and access management, encryption, and data protection  \nEXAM PREP: AWS CERTIFIED MACHINE LEARNING ENGINEER  \nCourse Code: 910030  \nVIRTUAL CLASSROOM LIVE $899 CAD 1 Day  \nVirtual Classroom Live Outline  \nModule 1: Data Preparation for Machine Learning (ML)  \n• 1.1 Ingest and store data.  \n• 1.2 Transform data and perform feature engineering.  \n• 1.3 Ensure data integrity and prepare data for modeling  \nModule 2:ML Model Development  \n• 2.1 Choose a modeling approach.  \n• 2.2 Train and refine models.  \n• 2.3 Analyze model performance.  \nModule 3: Deployment and Orchestration of ML Workflows  \n• 3.1 Select deployme","cbCailL0KaIVi84G","https://ap.wps.com/l/cbCailL0KaIVi84G","pdf",123218,"English","# What You’ll Learn\n# Who Needs to Attend\n# Prerequisites\n## General IT Knowledge\n## Recommended AWS Knowledge\n# Virtual Classroom Live Outline\n## Module 1: Data Preparation for Machine Learning (ML)\n## Module 2: ML Model Development\n## Module 3: Deployment and Orchestration of ML Workflows\n## Module 4: ML Solution Monitoring, Maintenance, and Security","[{\"question\":\"What does the AWS Certified Machine Learning Engineer-Associate (MLA-C01) exam validate?\",\"answer\":\"It validates the ability to build, operationalize, and maintain machine learning solutions and pipelines using the AWS Cloud.\"},{\"question\":\"Does the course require prior training before attendance?\",\"answer\":\"No specific training is required before taking the course, but prerequisite knowledge is recommended for best preparation.\"},{\"question\":\"What topics are covered in the virtual classroom live modules?\",\"answer\":\"Modules include data preparation, ML model development, deployment and orchestration of ML workflows, and monitoring, maintenance, and security for ML solutions.\"}]","EXAM PREP - AWS Certified Machine Learning Engineer - Associate (MLA-C01) - Course Code 910030 | PDF",1789934645]