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Certification earners have the practical knowledge needed to implement GenAI solutions into production environments using AWS technologies. They demonstrated the ability to design and build solutions using various GenAI architectures, integrate FMs into applications and business workflows, and implement agentic AI solutions. They are able to apply prompt engineering techniques, optimize for cost and performance, and configure in line with security, governance, and Responsible AI practices.

Earners of this certification have an extensive understanding of implementing continuous delivery systems and methodologies on the AWS Cloud. They demonstrated the ability to automate resilient applications and infrastructure deployments using AWS services. Badge owners are able to enforce policies, implement automated monitoring, and configure event management of an environment.

Earners of this certification have an extensive understanding of designing technical strategies to accomplish specific business goals. They demonstrated the ability to balance best practices and trade-offs based on business context. Badge owners are able to design solutions across multiple platforms and providers.

Earners of this certification have the ability to design and maintain network architecture for all AWS services. They demonstrated the ability to leverage tools to automate AWS networking tasks based on business needs. Badge owners are able to design and implement AWS and hybrid IT network architectures at scale.

Earners of this certification have an in-depth understanding of AWS security services and the shared responsibility model (between AWS and the customer). They demonstrated the ability to design, implement, and troubleshoot various security models in the AWS Cloud. Badge owners can leverage various security models for organizations of all sizes.

Earners of this certification have an in-depth understanding of how to use AWS services to implement data pipelines and to monitor, troubleshoot, and optimize cost and performance issues in accordance with best practices. Badge owners have technical expertise to understand the effects of volume, variety, and velocity on data ingestion. They are familiar with transformation, modeling, security, governance, privacy, schema design, and optimal data store design.

Earners of this certification have a comprehensive understanding of application life-cycle management. They demonstrated proficiency in writing applications with AWS service APIs, AWS CLI, and SDKs; using containers; and deploying with a CI/CD pipeline. Badge owners are able to develop, deploy, and debug cloud-based applications that follow AWS best practices.

Earners of this certification have a comprehensive understanding of how to deploy, manage, and operate workloads on AWS. They demonstrated the ability to monitor, scale, and secure systems in the AWS Cloud. They can implement optimizations based on business requirements of performance and cost. Badge owners are able to provide guidance on implementing best practices for cloud operations.

Earners of this certification have a comprehensive understanding of AWS services and technologies. They demonstrated the ability to build secure and robust solutions using architectural design principles based on customer requirements. Badge owners are able to strategically design well-architected distributed systems that are scalable, resilient, efficient, and fault-tolerant.

Earners of this badge have knowledge and skills in developing, deploying, maintaining, and monitoring ML solutions to meet AI/ML objectives. They know how to ingest, transform, validate, and prepare data for ML modeling. They have skills in implementing and operationalizing ML workloads in production. They can select modeling approaches and analyze model performance. They have the expertise to monitor ML solutions and to secure ML systems and resources.

Earners of this certification have a fundamental understanding of IT services and their uses in the AWS Cloud. They demonstrated cloud fluency and foundational AWS knowledge. Badge owners are able to identify essential AWS services necessary to set up AWS-focused projects.

Earners of this badge understand AI, ML, and generative AI concepts, methods, and strategies in general and on AWS. They can determine the correct types of AI/ML technologies to apply to specific use cases and know how to use AI, ML, and generative AI technologies responsibly. They are familiar with the AWS Global Infrastructure, core AWS services and use cases, AWS service pricing models, and the AWS shared responsibility model for security and compliance in the AWS Cloud.

Earners of this badge have passed the AWS Data Lakehouse Demonstrated exam lab. Recipients completed timed challenges in a simulated business scenario and a live AWS environment. This exam validates skills in diagnosing and fixing real-time streaming infrastructure using Amazon Kinesis Data Streams, Amazon MSK, Amazon Data Firehose, Amazon Managed Service for Apache Flink, and AWS Glue.

Earners of this badge have passed the AWS Data Lakehouse Demonstrated exam lab. Recipients completed timed challenges in a simulated business scenario and a live AWS environment. This exam validates skills in diagnosing issues and implementing solutions across a lakehouse architecture using analytics services including Amazon Athena, AWS Glue, Apache Iceberg, Amazon EventBridge, AWS Lake Formation, and Amazon Data Firehose.

Earners of this badge have passed the AWS Incident Response Demonstrated exam lab. Passing this microcredential assessment represents a proficiency in identifying, containing, and remediating common AWS security incidents. Recipients completed timed challenges stemming from a simulated business scenario in a hands-on AWS environment.

Earners of this badge have passed the AWS Application Networking Demonstrated exam lab. Passing this microcredential assessment represents a proficiency in core tasks related to application delivery, performance optimization, and modern application architecture implementation. Recipients completed timed challenges stemming from a simulated business scenario in a hands-on AWS environment.

Earners of this badge have passed the AWS MLOps exam lab. Passing this microcredential assessment represents proficiency in using Amazon SageMaker AI to deploy foundation models and fine-tune ML models, run inference, configure observability, and scale applications.

Earners of this badge have passed the AWS Agentic AI Demonstrated exam lab. Passing this microcredential assessment represents a proficiency in several AWS Services involving agentic AI. Recipients completed timed challenges involving troubleshooting, repairing, integrating, and enhancing Bedrock Agents and Bedrock Guardrails in an AWS environment.

Earners of this badge have passed the AWS Serverless Demonstrated exam lab. Passing this microcredential assessment represents a proficiency in several AWS Services involving serverless architectures. Recipients completed timed challenges in a hands-on AWS environment using services including AWS Lambda, Amazon API Gateway, AWS Step Functions, and Amazon DynamoDB.

ログは各サービスにあっても、障害調査で困るのは「経路のつながりが見えない」こと。そこをAthenaでの段階的絞り込み → Bedrock AgentCoreによる調査支援の仕組み化、という流れで解決していく話。

# お知らせ 2026/08/17 イベントページを公開しました 2026/09/12 LT枠の登壇タイトルをタイムテーブルに反映しました 2026/09/13 参加登録・キャンセルは、9/17(木) 12:00 am までにお願いします 2026/09/17 参加登録を締め切りました # 🪄JAWS-UG YOKOHAMA & JAWS-UG SRE支部 共催！ 🪄 システムを動かし続けること、信頼を積み重ねること。 その現場にはいつも、地道な工夫と試行錯誤があります。 今回は、JAWS-UG YOKOHAMA と JAWS-UG SRE支部 の共催で、そんな"現場の...

はじめに 2026年9月6日（日）深夜2時、JAWS SONICの「深夜のAWS Gameday」というセッションで、AWS GameDayに参加してきました。JAWS SONICはJAWS-UG（AWS User Group – Japan）主催の24時間オンラインリレ...

はじめに 何かしらの業務に携わるベンダー資格を取得するために勉強している方、こんな経験をしたことはないですか？ 「マイナーすぎる or 情報がなさすぎる…問題集があっても少ない…」 「同じ問題ばっかり…もっとバリエーションが欲しい」 「新設資格で模擬試験がまだ少ない…」...

はじめに OneNoteで要求仕様書や設計書を書いているプロジェクトがある、という話を聞きました。 「それ、設計書として成立するの？」と思いながらも、ふと気づきました。 そもそも設計書って、何の情報があればいいんだろう、と。 フォーマットや記法の話になりがちですが、突き詰...

はじめに AWSで運用しているシステムのログ、毎日確認するの大変ですよね。 Lambda関数のエラーログ、ECSコンテナの実行ログ、API Gatewayのアクセスログ...。 CloudWatch Logsに蓄積されるログを毎日チェックして、エラーが出てないか、パフォー...

はじめに 2025年のAWS re:Inventに参加してきた内容を体験レポートとしてまとめました。 背景に関わるので簡単に自己紹介をしますと、私は2019年度に入社し、当時はシステム設計の研究開発を行う部門に所属していました。その後2022年度に家電関係の量産開発の設計...

三菱電機の空調・換気・衛生サイトです。フロン法点検支援・機器管理ツール「MELflo（メルフロー）」は、機器データを記録・共有して、機器管理やフロン　排出抑制法で定められた簡易点検などをサポートいたします。

三菱電機の空調・換気・衛生サイトです。空調機器管理ツール「MELflo」　のサービス「AirCoNet」は、空調機をインターネットに接続して、遠隔地から機器の　稼働状況や異常情報が確認できます。

三菱電機がおススメする情報提供サービス MELく～るLINK for スリムをご案内しています。空調冷熱製品の異常、運転データ、冷媒漏えいを遠隔で確認することができます。