[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-317422-105":59,"doc-detail-317422-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","aws-certified-machine-learning-engineer-associate-mla-c01-exam-guide","AWS Certified Machine Learning Engineer-Associate (MLA-C01) - Exam Guide","","AWS Certified Machine Learning Engineer-Associate (MLA-C01) exam guide explains how the AWS exam assesses the ability to build, operationalize, deploy, and maintain machine learning solutions and pipelines using the AWS Cloud. It outlines in-scope responsibilities such as preparing data, selecting modeling approaches, training and tuning, managing versions, configuring deployment infrastructure, implementing CI/CD for ML workflows, monitoring systems, and securing ML resources. It also states candidate experience expectations, question types, scoring rules, and pass criteria.",{"@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/aws-certified-machine-learning-engineer-associate-mla-c01-exam-guide/317422/",{"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/aws-certified-machine-learning-engineer-associate-mla-c01-exam-guide/317422.png","ImageObject",300,407,{"name":92,"@type":93},"Chumphorn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-20",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What skills does the MLA-C01 exam validate for candidates?","Question",{"text":113,"@type":114},"The exam validates the ability to build, operationalize, deploy, and maintain machine learning solutions and pipelines on AWS. It covers data preparation, modeling, deployment, CI/CD automation, monitoring, and securing ML systems and resources.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What experience is recommended for the target candidate?",{"text":118,"@type":114},"Candidates should have at least 1 year of experience using Amazon SageMaker and other AWS services for ML engineering. They should also have at least 1 year in a related role such as backend software developer, DevOps developer, data engineer, or data scientist.",{"name":120,"@type":111,"acceptedAnswer":121},"How are questions scored and what is the passing score?",{"text":122,"@type":114},"The exam includes 50 scored questions that affect the score, and unanswered questions are scored as incorrect with no penalty for guessing. Results are reported as a scaled score from 100–1,000, and the minimum passing score is 720.","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},317422,1790118280,{"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":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},2336475401981,"https://ap-avatar.wpscdn.com/avatar/22000c94efd8d5204d?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786935347598174694","AWS Certified Machine Learning Engineer-Associate (MLA-C01)  \nExam Guide  \nIntroduction  \nThe AWS Certified Machine Learning Engineer-Associate (MLA-C01) exam validates a candidate’s ability to build, operationalize, deploy, and maintain machine learning (ML) solutions and pipelines by using the AWS Cloud.  \nThe exam also validates a candidate’s ability to complete the following tasks:  \n􀁸 Ingest, transform, validate, and prepare data for ML modeling.  \n􀁸 Select general modeling approaches, train models, tune hyperparameters, analyze model performance, and manage model versions.  \n􀁸 Choose deployment infrastructure and endpoints, provision compute resources, and configure auto scaling based on requirements.  \n􀁸 Set up continuous integration and continuous delivery (CI/CD) pipelines to automate orchestration of ML workflows.  \n􀁸 Monitor models, data, and infrastructure to detect issues.  \n􀁸 Secure ML systems and resources through access controls, compliance features, and best practices.  \nTarget candidate description  \nThe target candidate should have at least 1 year of experience using Amazon SageMaker and other AWS services for ML engineering. The target candidate also should have at least 1 year of experience in a related role such as a backend software developer, DevOps developer, data engineer, or data scientist.  \nRecommended general IT knowledge  \nThe target candidate should have the following general IT knowledge:  \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  \nVersion 1.0 MLA-C01 1 | PAGE  \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 CI/CD pipelines and infrastructure as code (IaC)  \n􀁸 Experience with code repositories for version control and CI/CD pipelines  \nRecommended AWS knowledge  \nThe target candidate should have the following AWS knowledge:  \n􀁸 Knowledge of SageMaker 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  \nJob tasks that are out of scope for the target candidate  \nThe following list contains job tasks that the target candidate is not expected to be able to perform. This list is non-exhaustive. These tasks are out of scope for the exam:  \n􀁸 Designing and architecting full end-to-end ML solutions  \n􀁸 Setting up best practices and guiding ML strategies  \n􀁸 Handling integration with a wide array of services or new tools and technologies  \n􀁸 Working deeply in two or more ML domains (for example, natural language processing [NLP], computer vision)  \n􀁸 Quantizing models and analyzing the impact on accuracy  \nRefer to the Appendix for a list of in-scope AWS services and features and a list of out-of-scope AWS services and features.  \nVersion 1.0 MLA-C01 2 | PAGE  \nExam content  \nQuestion types  \nThe exam contains one or more of the following question types:  \n􀁸 Multiple choice: Has one correct response and three incorrect responses (distractors) .  \n􀁸 Multiple response: Has two or more correct responses out of five or more response options. You must select all the correct responses to receive credit for the question.  \n􀁸 Ordering: Has a list of 3–5 responses to complete a specified task. You must select the correct responses and place the responses in the correct order to receive cred","cbCaic6uXahZLj5I","https://ap.wps.com/l/cbCaic6uXahZLj5I","pdf",219817,23,"English","# Introduction\n## Target candidate description\n## Recommended general IT knowledge\n## Recommended AWS knowledge\n## Job tasks that are out of scope for the target candidate\n# Exam content\n## Question types\n## Unanswered and unscored content\n## Exam results","[{\"question\":\"What skills does the MLA-C01 exam validate for candidates?\",\"answer\":\"The exam validates the ability to build, operationalize, deploy, and maintain machine learning solutions and pipelines on AWS. It covers data preparation, modeling, deployment, CI/CD automation, monitoring, and securing ML systems and resources.\"},{\"question\":\"What experience is recommended for the target candidate?\",\"answer\":\"Candidates should have at least 1 year of experience using Amazon SageMaker and other AWS services for ML engineering. They should also have at least 1 year in a related role such as backend software developer, DevOps developer, data engineer, or data scientist.\"},{\"question\":\"How are questions scored and what is the passing score?\",\"answer\":\"The exam includes 50 scored questions that affect the score, and unanswered questions are scored as incorrect with no penalty for guessing. Results are reported as a scaled score from 100–1,000, and the minimum passing score is 720.\"}]","AWS Certified Machine Learning Engineer-Associate (MLA-C01) - Exam Guide | PDF",1789932711,58]