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通过模拟题、动手实验和官方模拟考试,评估并弥补 AWS Certified Developer–Associate(DVA-C02)的备考差距。
Learn Amazon Connect Customer Profiles fundamentals, including core concepts, use cases, and security and cost considerations for real-world implementation.
Amazon DocumentDB クラスターの設計、監視、トラブルシューティングを通じて、データベースのパフォーマンスを最適化する方法を学ぶ。
AWS Migration Hub を使い、オンプレミス環境の検出・評価から移行のオーケストレーション、追跡、アプリケーションのリファクタリングまでを進める方法を学びます。
AWS上で生成AIアプリケーションを構築・自動化し、LLMのファインチューニングやベクトルデータベース活用を実践するゲームベースのシミュレーション。
Train machine learning models in Amazon SageMaker by selecting compute environments, using built-in algorithms and script mode, and reducing training time.
Refine machine learning models through regularization, ensemble methods, hyperparameter optimization, pruning, fine-tuning, and versioning with Amazon SageMaker.
Implement proficiency-based, priority, and data-driven routing strategies in Amazon Connect contact centers.
Analyze machine learning model performance with classification and regression metrics, convergence diagnostics, and Amazon SageMaker tools.
Choose cost-efficient, interpretable machine learning approaches across AWS AI services, SageMaker algorithms, JumpStart solutions, and Bedrock built-in solutions.
Explore data engineering roles, discovery, architectures, pipelines, security, and AWS tools including CI/CD, IaC, serverless applications, and cost optimization.
Hands-on SageMaker lab for training models with built-in XGBoost and custom script-mode code, importing dependencies, tuning hyperparameters, and evaluating predictions.
Hands-on lab for visualizing, cleaning, and transforming datasets with SageMaker Data Wrangler, then querying them with Spark on Amazon EMR.
AWS에서 CI/CD, 인프라 자동화, 컨테이너 및 서버리스 배포를 구현하는 DevOps 엔지니어링 과정
Plan and perform lift-and-shift migrations to AWS using assessment, discovery, migration, and tracking services.
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