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Exam Prep: AWS Certified Cloud Practitioner

Written by Gemma Corrado on . Posted in AWS Training, Training.


Exam Prep: AWS Certified Cloud Practitioner

Please find our upcoming course dates at the end of this page!

Course description

In this 1-day instructor-led training course, you will prepare for the AWS Certified Cloud Practitioner exam. This course helps you understand what is important for the exam, how to approach different question types, and how to identify common misconceptions that appear on the test. Our instructor will guide you through exam-focused reviews of each domain, highlighting frequently tested concepts, clarifying easily confused topics, and providing strategic approaches to question analysis. Through targeted practice with exam-style questions, you will develop the specific knowledge and test-taking skills needed to succeed on the certification exam.

COURSE OBJECTIVES

In this course, you will:

  • Identify the scope and content tested by the AWS Certified Cloud Practitioner exam
  • Apply effective test-taking strategies for different question types
  • Practice with exam-style questions to build confidence and identify knowledge gaps
  • Understand key concepts across all exam domains including cloud concepts, security and compliance, cloud technology and services, and billing, pricing, and support

INTENDED AUDIENCE

This course is intended for:

  • individuals with up to 6 months of exposure to AWS Cloud design, implementation, and/or operations who are preparing to take the AWS Certified Cloud Practitioner exam

Prerequisites

To be successful in this course, you should have:

  • Up to 6 months of exposure to AWS Cloud design, implementation, and/or operations
  • Basic understanding of IT services and their uses in the AWS Cloud platform
  • Some familiarity with AWS services and the AWS Management Console

    ACTIVITIES

    This course includes:

    • Presentations
    • Discussions
    • Guided practice with exam-style questions
    • Scenario-based discussions

    COURSE DURATION / PRICE

    • 1 day
    • € 750.00 (excl. tax) per person (DE)

    Course outline

    Day 1

    • Module 0: Introduction and Exam Overview
      • About this course
      • About the exam
        • Format and structure
        • Question types and scoring
        • Test-taking strategies
        • Preparation resources
    • Module 1: Cloud Concepts
      • Define the benefits of the AWS Cloud
      • Identify design principles of the AWS Cloud
      • Understand the benefits of and strategies for migration to the AWS Cloud
      • Understand concepts of cloud economics
      • Walkthrough of exam-style questions
      • Additional questions
    • Module 2: Security and Compliance
      • Understand the AWS shared responsibility model
      • Understand AWS Cloud security, governance, and compliance concepts
      • Identify AWS access management capabilities
      • Identify components and resources for security
      • Walkthrough of exam-style questions
      • Additional questions
    • Module 3: Cloud Technology and Services
      • Define methods of deploying and operating in the AWS Cloud
      • Define the AWS global infrastructure
      • Identify AWS compute services
      • Identify AWS database services
      • Identify AWS network services
      • Identify AWS storage services
      • Identify AWS artificial intelligence and machine learning (AI/ML) services and analytics services
      • Identify services from other in-scope AWS service categories
      • Walkthrough of exam-style questions
      • Additional questions
    • Module 4: Billing, Pricing, and Support
      • Compare AWS pricing models
      • Understand resources for billing, budget, and cost management
      • Identify AWS technical resources and AWS Support options
      • Walkthrough of exam-style questions
      • Additional questions
    • Module 5: Course Wrap-up
      • Review of exam domains
      • Final test-taking strategies
      • Additional preparation resources
      • Next steps

    IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

    Course materials are in English, on request also in German (if available).
    The Course language is German, on request also in English.

    Neue Termine in Planung!

    Continue reading

    Agentic AI Foundations

    Written by Gemma Corrado on . Posted in AWS Training, Training.


    Agentic AI Foundations

    Please find our upcoming course dates at the end of this page!

    Course description

    In this course, you’ll explore the core principles and strategies for designing Agentic AI systems using AWS services. You’ll learn how Agentic AI differs from traditional conversational systems, and how to use tools like Amazon Q, Kiro, Amazon Bedrock Agents, and Amazon Bedrock AgentCore to build autonomous, goal-driven solutions that solve real-world problems.

    COURSE OBJECTIVES

    In this course, you will learn to:

    • Summarize the evolution of Agentic AI and define what makes something “agentic” 
    • Identify core components of agentic systems
    • Distinguish between workflow, autonomous, and hybrid agents
    • Compare AWS service options for Agentic AI
    • Describe capabilities and use cases of Amazon Q Developer, Amazon Q Business, and Kiro
    • Explain Amazon Bedrock AgentCore and Amazon Bedrock Agents fundamentals
    • Identify basic implementation patterns for Agentic AI
    • Describe observability and interoperability patterns for production agentic AI systems

    INTENDED AUDIENCE

    This course is intended for:

    • Software developers new to Agentic AI seeking foundational knowledge
    • Technical professionals exploring AI capabilities and interested in core components and applications of agentic AI
    • Development teams evaluating Agentic AI solutions and needing to differentiate between agent types
    • AWS Users expanding into Agentic AI, including current users of Amazon Q Developer, Amazon Q Business, and Amazon Bedrock Agents

    Prerequisites

    We recommend that attendees of this course have:

      ACTIVITIES

      This course includes:

      • Presentations
      • Hands-on lab,
      • Group exercises

      COURSE DURATION / PRICE

      • 1 day
      • € 750.00 (excl. tax) per person (DE)

      Course outline

      Day 1

      • Module 1: From LLMs to Agents
        • Understanding Large Language Models (LLMs)
        • Innovations powering agents
        • Evolution timeline from LLMs to Agents
      • Module 2: Exploring Agentic AI
        • Understanding Agentic AI
        • Types of AI agents
        • Agentic AI applications
      • Module 3: Understanding Agentic AI Workflows
        • Workflow patterns
        • Amazon Bedrock flows overview
      • Module 4: Introducing Autonomous Agents
        • How Autonomous Agents work
        • ReAct
        • ReWoo
        • Multi-agent collaboration
        • AWS Agentic AI solutions
      • Module 5: Amazon Q and Agentic Development Tools
        • Amazon Q Developer
        • Amazon Q Business
        • Amazon Q in AWS Services
        • Kiro: AI-powered IDE with spec-driven development
      • Module 6: Agentic AI with Amazon Bedrock
        • Amazon Bedrock Agents
        • Amazon Bedrock AgentCore
        • Hands-on lab: Explore Amazon Bedrock Agents integrated with Amazon Bedrock Knowledge Bases and Amazon Bedrock Guardrails
      • Module 7: Building DIY Solutions
        • DIY solutions
        • Observability and Monitoring
        • Agent Interoperability
      • Module 8: Course Wrap-up
        • Next steps and additional resources
        • Course summary

      IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

      Course materials are in English, on request also in German (if available).
      The Course language is German, on request also in English.

      Neue Termine in Planung!

      Continue reading

      Exam Prep: AWS Certified AI Practitioner

      Written by Gemma Corrado on . Posted in AWS Training, Training.


      Exam Prep: AWS Certified AI Practitioner

      Please find our upcoming course dates at the end of this page!

      Course description

      This intermediate-level course prepares you for the AWS Certified AI Practitioner 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 hone your test-taking abilities. By the end, you’ll have a firm grasp on the concepts and practical applications tested on the AWS Certified AI Practitioner certification exam.

      COURSE OBJECTIVES

      In this course, you will learn to:

      • Identify the scope and content tested by the AWS Certified AI Practitioner exam.
      • Practice exam-style questions and evaluate your preparation strategy.
      • Examine use cases and differentiate between them.

      INTENDED AUDIENCE

      This course is intended for:

      • individuals who are preparing for the AWS Certified AI Practitioner exam

      Prerequisites

      You are not required to take any specific training before taking this course. However, the following
      prerequisite knowledge is recommended prior to taking the AWS Certified Machine Learning Engineer –
      Associate exam.

      Recommended AWS knowledge

      • Familiarity with the core AWS services (for example, Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker AI) and AWS core services use cases.
      • Suggested to have up to 6 months of exposure to AI and ML technologies on AWS.
      • Are familiar with, but do not necessarily build, solutions using AI and ML technologies on AWS.
      • Familiarity with the AWS shared responsibility model for security and compliance in the AWS Cloud.
      • Familiarity with AWS Identity and Access Management (IAM) for securing and controlling access to AWS resources.
      • Familiarity with the AWS global infrastructure, including the concepts of AWS Regions, Availability Zones, and edge locations.
      • Familiarity with AWS service pricing models.

      Recommended courses

      The following courses (or similar) are recommended but not required.

      • Fundamentals of Machine Learning and Artificial Intelligence (1 hour)
      • Exploring Artificial Intelligence Use Cases and Applications (1 hour)
      • Responsible Artificial Intelligence Practices (1 hour)
      • Developing Machine Learning Solutions (1 hour)
      • Developing Generative Artificial Intelligence Solutions (1 hour)
      • Essentials of Prompt Engineering (1 hour)
      • Optimizing Foundation Models (1 hour)
      • Security, Compliance, and Governance for AI Solutions (1 hour)
      • Generative AI for Executives (0.25 hour)
      • Amazon Q Business Getting Started (0.75 hour)
      • Amazon Bedrock Getting Started (1 hour)
      • Getting Started with Amazon Comprehend: Custom Classification (1.25 hours)
      • Build a Question-Answering Bot Using Generative AI (1.5 hours)

      ACTIVITIES

      This course includes:

      • Subject overview presentations
      • Exam-style questions
      • Use cases
      • Group discussions and activities

      COURSE DURATION / PRICE

      • 1 day
      • € 775.00 (excl. tax) per person (DE)

      Course outline

      Day 1

      • Domain 1: Fundamentals of AI and ML
        • Explain basic AI concepts and terminologies
        • Identify practical use cases for AI
        • Describe the ML development lifecycle
      • Domain 2: Fundamentals of Generative AI
        • Explain the basic concepts of generative AI
        • Understand the capabilities and limitations of generative AI for solving business problems
        • Describe AWS infrastructure and technologies for building generative AI applications
      • Domain 3: Applications of Foundation Models
        • Describe design considerations for applications that use foundation models
        • Choose effective prompt engineering techniques
        • Describe the training and fine-tuning process for foundation models
        • Describe methods to evaluate foundation model performance
      • Domain 4: Guidelines for Responsible AI
        • Explain the development of AI systems that are responsible
        • Recognize the importance of transparent and explainable models
      • Domain 5: Security, Compliance, and Governance for AI Solutions
        • Explain methods to secure AI systems
        • Recognize governance and compliance regulations for AI systems

      IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

      Course materials are in English, on request also in German (if available).
      The Course language is German, on request also in English.

      Neue Termine in Planung!

      Continue reading

      Exam Prep: AWS Certified Machine Learning Engineer – Associate

      Written by Gemma Corrado on . Posted in AWS Training, Training.


      Exam Prep: AWS Certified Machine Learning Engineer – Associate

      Please find our upcoming course dates at the end of this page!

      Course description

      This intermediate-level course prepares you for the AWS Certified Machine Learning Engineer – Associate 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 examstyle 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 hone your 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 Engineer – Associate exam.

      COURSE OBJECTIVES

      In this course, you will learn to:

      • Identify the scope and content tested by the AWS Certified Machine Learning Engineer – Associate exam.
      • Practice exam-style questions and evaluate your preparation strategy.
      • Examine use cases and differentiate between them.

      INTENDED AUDIENCE

      This course is intended for:

      • individuals who are preparing for the AWS Certified Machine Learning
        Engineer – Associate exam

      Prerequisites

      You are not required to take any specific training before taking this course. However, the following
      prerequisite knowledge is recommended prior to taking the AWS Certified Machine Learning Engineer –
      Associate exam.

      General IT knowledge
      Learners are recommended to have the following:

      • Suggested 1 year of experience in a related role such as a backend software developer, DevOps
        developer, data engineer, or data scientist.
      • Basic understanding of common ML algorithms and their use cases
      • Data engineering fundamentals, including knowledge of common data formats, ingestion, and
        transformation to work with ML data pipelines
      • Knowledge of querying and transforming data
      • Knowledge of software engineering best practices for modular, reusable code development,
        deployment, and debugging
      • Familiarity with provisioning and monitoring cloud and on-premises ML resources
      • Experience with continuous integration and continuous delivery (CI/CD) pipelines and
        infrastructure as code (IaC)
      • Experience with code repositories for version control and CI/CD pipelines

      Recommended AWS knowledge
      Learners are recommended to be able to do the following:

      • Suggested 1 year of experience using Amazon SageMaker AI and other AWS services for ML
        engineering.
      • Knowledge of Amazon SageMaker AI capabilities and algorithms for model building and
        deployment
      • Knowledge of AWS data storage and processing services for preparing data for modeling
      • Familiarity with deploying applications and infrastructure on AWS
      • Knowledge of monitoring tools for logging and troubleshooting ML systems
      • Knowledge of AWS services for the automation and orchestration of CI/CD pipelines
      • Understanding of AWS security best practices for identity and access management, encryption,
        and data protection

      ACTIVITIES

      This course includes:

      • Subject overview presentations
      • Exam-style questions
      • Use cases
      • Group discussions and activities

      COURSE DURATION / PRICE

      • 1 day
      • € 775.00 (excl. tax) per person (DE)

      Course outline

      Day 1

      • Domain 1: Data Preparation for Machine Learning (ML)
        • Ingest and store data.
        • Transform data and perform feature engineering.
        • Ensure data integrity and prepare data for modeling.
      • Domain 2: ML Model Development
        • Choose a modeling approach.
        • Train and refine models.
        • Analyze model performance.
      • Domain 3: Deployment and Orchestration of ML Workflows
        • Select deployment infrastructure based on existing architecture and requirements.
        • Create and script infrastructure based on existing architecture and requirements.
        • Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines.
      • Domain 4: ML Solution Monitoring, Maintenance, and Security
        • Monitor model interference.
        • Monitor and optimize infrastructure costs.
        • Secure AWS resources.

      IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

      Course materials are in English, on request also in German (if available).
      The Course language is German, on request also in English.

      Neue Termine in Planung!

      Continue reading

      Machine Learning Engineering on AWS

      Written by Gemma Corrado on . Posted in AWS Training, Training.


      Machine Learning Engineering on AWS

      Please find our upcoming course dates at the end of this page!

      Course description

      Machine Learning (ML) Engineering on AWS is a 3-day intermediate course designed for ML professionals seeking to learn machine learning engineering on AWS. Participants learn to build, deploy, orchestrate, and operationalize ML solutions at scale through a balanced combination of theory, practical labs, and activities. Participants will gain practical experience using AWS services such as Amazon SageMaker AI and analytics tools such as Amazon EMR to develop robust, scalable, and production-ready machine learning applications.

      COURSE OBJECTIVES

      In this course, you will learn to do the following:

      • Explain ML fundamentals and its applications in the AWS Cloud.
      • Process, transform, and engineer data for ML tasks by using AWS services.
      • Select appropriate ML algorithms and modeling approaches based on problem
        requirements and model interpretability.
      • Design and implement scalable ML pipelines by using AWS services for model training,
        deployment, and orchestration.
      • Create automated continuous integration and delivery (CI/CD) pipelines for ML workflows.
      • Discuss appropriate security measures for ML resources on AWS.
      • Implement monitoring strategies for deployed ML models, including techniques for
        detecting data drift.

      INTENDED AUDIENCE

      This course is intended for:

      • This course is designed for professionals who are interested in building, deploying, and
        operationalizing machine learning models on AWS. This could include current and in-training
        machine learning engineers who might have little prior experience with AWS.
      • DevOps Engineer
      • Developer
      • SysOps Engineer

      Prerequisites

      We recommend that attendees of this course have the following:

      • Familiarity with basic machine learning concepts
      • Working knowledge of Python programming language and common data science libraries
        such as NumPy, Pandas, and Scikit-learn
      • Basic understanding of cloud computing concepts and familiarity with AWS
      • Experience with version control systems such as Git (beneficial but not required)

      ACTIVITIES

      This course includes:

      • Presentations
      • Demonstrations
      • Group exercises
      • Hands-on labs

      COURSE DURATION / PRICE

      • 3 days
      • € 1,995.00 (excl. tax) per person (DE)

      Course outline

      • Day 1

        • Module 0: Course Introduction
        • Module 1: Introduction to Machine Learning (ML) on AWS
          • Introduction to ML
          • Amazon SageMaker AI
          • Responsible ML
        • Module 2: Analyzing Machine Learning (ML) Challenges
          • Evaluating ML business challenges
          • ML training approaches
          • ML training algorithms
        • Module 3: Data Processing for Machine Learning (ML)
          • Data preparation and types
          • Exploratory data analysis
          • AWS storage options and choosing storage
        • Module 4: Data Transformation and Feature Engineering
          • Handling incorrect, duplicated, and missing data
          • Feature engineering concepts
          • Feature selection techniques
          • AWS data transformation services
          • Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR
          • Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK

      • Day 2

        • Module 5: Choosing a Modeling Approach
          • Amazon SageMaker AI built-in algorithms
          • Amazon SageMaker Autopilot
          • Selecting built-in training algorithms
          • Model selection considerations
          • ML cost considerations
        • Module 6: Training Machine Learning (ML) Models
          • Model training concepts
          • Training models in Amazon SageMaker AI
          • Lab 3: Training a model with Amazon SageMaker AI
        • Module 7: Evaluating and Tuning Machine Learning (ML) models
          • Evaluating model performance
          • Techniques to reduce training time
          • Hyperparameter tuning techniques
          • Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI
        • Module 8: Model Deployment Strategies
          • Deployment considerations and target options
          • Deployment strategies
          • Choosing a model inference strategy
          • Container and instance types for inference
          • Lab 5: Shifting Traffic

      • Day 3

        • Module 9: Securing AWS Machine Learning (ML) Resources
          • Access control
          • Network access controls for ML resources
          • Security considerations for CI/CD pipelines
        • Module 10: Machine Learning Operations (MLOps) and Automated Deployment
          • Introduction to MLOps
          • Automating testing in CI/CD pipelines
          • Continuous delivery services
          • Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio
        • Module 11: Monitoring Model Performance and Data Quality
          • Detecting drift in ML models
          • SageMaker Model Monitor
          • Monitoring for data quality and model quality
          • Automated remediation and troubleshooting
          • Lab 7: Monitoring a Model for Data Drift
        • Module 12: Course Wrap-up

      IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

      Course materials are in English, on request also in German (if available).
      The Course language is German, on request also in English.

      Neue Termine in Planung!

      Continue reading