This blog is your ultimate guide to achieving success in the AWS Certified Machine Learning Engineer – Associate [MLA-C01] certification. Follow our Step-by-Step Activity Guides to gain practical, in-demand machine learning skills, improve your CV, and confidently tackle job interviews.
Our hands-on labs are carefully designed to help you not only master machine learning on AWS you can conquer the AWS Certified Machine Learning Engineer – Associate [MLA-C01] exam. With these labs, you’ll be prepared for real-world machine-learning tasks.
List of Labs Included in Our AWS Certified Machine Learning Engineer Program:
- Lab 1: Create an AWS Free Trial Account
- Lab 2: CloudWatch – Create Billing Alarm & Service Limits
- Lab 1: Create S3 Bucket, Upload Files, and Host a Website
- Lab 2: S3 Cross-Region Replication
- Lab 3: S3 Lifecycle Management on S3 Bucket
- Lab- 4: Build ETL Jobs with AWS Glue
1.3 Amazon EBS and Kinesis Data Streams
- Lab 1: Create and Manage EBS Volume and Snapshot
- Lab 2: Create and Mount Elastic File System (EFS) on EC2
- Lab 3: Creating Windows File Systems Using Amazon FSx
- Lab 4: Amazon Kinesis Data Streams – Hands-On
1.4 Data Transformation, Integrity, and Feature Engineering
- Lab 1:Preparing Data for TF-IDF using Sagemaker Notebook
- Lab 2: Glue DataBrew
1.5 Amazon SageMaker and Its Built-In Algorithms
- Lab 1: Setting Up Jupyter Notebook Environment in SageMaker Studio
- Lab 2: Create & Manage SageMaker Studio: Deploy & Test SageMaker JumpStart Foundation Models
- Lab 3: SageMaker Studio, Canvas, and Data Wrangler
1.6 Generative AI Model Fundamentals
- Lab 1: Exploring Transformers: Tokenization, Self-Attention, and Text Generation with BERT and GPT-2
1.7 Developing Generative AI Applications with Bedrock
- Lab 1: Chat, Text, and Image Foundation Models in the Bedrock Playground
- Lab 2: Building and Querying a RAG System with Amazon Bedrock Knowledge Bases
- Lab 3: Building and Testing Guardrails with Amazon Bedrock
- Lab 4: Build a Bedrock Agent with Action Groups, Knowledge Bases, and Guardrails
- Lab 1: Create ECR, Install Docker, Create Image, and Push Image To ECR
- Lab 2: Amazon EKS: How to Create Cluster
- Lab 3: AWS CloudFormation – Hands On
- Lab 4: AWS CDK – Hands On
- Lab 5: Amazon EventBridge – Hands On
1.9 Security, Identity, and Compliance
- Lab 1: Working with AWS IAM
- Lab 2: Enable Multi-Factor Authentication
- Lab 3: KMS: Create and Use
- Lab 4: AWS Secrets Manager
1.10 Management and Governance
- Lab 1: Get Started with AWS X-Ray
- Lab 2: Enable CloudTrail and Store Logs in S3
- Lab 3: Setting Up AWS Config
1.1 AWS Basic Labs
Lab 1: Create an AWS Free Trial Account
Embark on your AWS journey by setting up a free trial account. This hands-on lab guides you through the initial steps of creating an AWS account, giving you access to a plethora of cloud services to experiment and build with.
Amazon Web Services (AWS) is providing a free trial account for 12 months to new subscribers to get hands-on experience with all the services that AWS provides. Amazon is giving us a number of different services that we can use, with some limitations, to get hands-on practice and gain more knowledge on AWS Cloud services as well as regular business use.
With the AWS Free Tier account, all the services offered have limited usage limits on what we can use without being charged. Here, we will look at how to register for an AWS FREE Tier Account.
To learn how to create a free AWS account, check our Step-by-step blog, How To Create AWS Free Tier Account
Lab 2: CloudWatch – Create Billing Alarm & Service Limits
Dive into CloudWatch, AWS’s monitoring service. This lab focuses on setting up billing alarms to manage costs effectively and keeping an eye on service limits to ensure your applications run smoothly within defined boundaries.
AWS billing notifications can be enabled using Amazon CloudWatch. CloudWatch is an Amazon Web Services service that monitors all of your AWS account activity. CloudWatch, in addition to billing notifications, provides infrastructure for monitoring apps, logs, metrics collection, and other service metadata, as well as detecting activity in your AWS account usage.
AWS CloudWatch offers a number of metrics through which you can set your alarms. For example, you may set an alarm to warn you when a running instance’s CPU or memory utilisation exceeds 90% or when the invoice amount exceeds $100. We get 10 alarms and 1,000 email notifications each month with an AWS free tier account.
To learn about CloudWatch, check our Step-by-step blog, CloudWatch vs. CloudTrail: Comparison, Working & Benefits
1.2 AWS Data Ingestion
Lab 1: Create an S3 Bucket and Upload Files
Objective: Learn how to create and manage an S3 bucket, upload data, and host files. This lab provides hands-on experience in managing data storage in AWS.
In this lab, you will create an S3 bucket, configure access policies, and practice uploading and managing various types of files. You’ll also learn how to set up permissions and version control for secure and efficient file management. Additionally, you will explore using S3 to host static websites.
By the end of this lab, you will have a solid understanding of S3, the ability to create a bucket, and the skills to upload and manage files in your AWS account.
To learn about S3 bucket, check our blog, AWS S3 Bucket | Amazon Simple Storage Service Bucket
Lab 2: S3 Cross-Region Replication
Objective: Learn how to replicate your S3 bucket across different AWS regions to ensure data availability and redundancy.
In this lab, you will configure cross-region replication for your S3 bucket, enabling automatic duplication of files to another AWS region. You will explore setting up versioning, configuring the replication rule, and verifying the replication process. This ensures data availability in the event of a regional failure, providing enhanced durability and backup.
By completing this lab, you will be proficient in setting up cross-region replication, a critical skill for ensuring the durability of your data.
To learn about S3 bucket, check our blog, AWS S3 Bucket | Amazon Simple Storage Service Bucket
Lab 3: S3 Lifecycle Management
Objective: Understand how to manage the data lifecycle in S3 by creating policies to automatically transition data to different storage classes and manage its retention.
In this lab, you will learn to create lifecycle policies in S3, which help automate the transition of data between storage classes like Standard, Infrequent Access, and Glacier. You’ll also explore setting up policies for data expiration and deletion, ensuring optimal data management and cost savings. By configuring these policies, you will be able to optimize storage costs based on the data’s usage patterns.
This lab helps you reduce costs by applying lifecycle policies that automate data transition between different S3 storage classes.
Lab- 4: Build ETL Jobs with AWS Glue
Objective: This lab is designed to provide hands-on experience in creating and managing ETL jobs using AWS Glue.
You will create an AWS Glue crawler to discover and catalog metadata, set up an ETL job to transform raw data, and automate these processes.
By the end, you will have the skills to build robust ETL workflows and automate data preparation for machine learning and analytics.
To learn about AWS Glue, check our blog, AWS Glue: Overview, Features, Architecture, Use Cases & Pricing
1.3 Amazon EBS and Kinesis Data Streams
Lab 1: Create and Manage EBS Volume and Snapshot
Objective: This lab is designed to provide hands-on experience in creating and managing EBS Volumes and Snapshots using AWS services. You will learn how to create different types of EBS volumes, such as General Purpose SSD, Cold HDD, and Magnetic Standard, and attach these volumes to EC2 instances. Additionally, you will modify the volume size and type as required, ensuring scalability and optimized performance.
The lab will guide you through creating and managing snapshots for data backup, disaster recovery, and creating Amazon Machine Images (AMI) for launching new EC2 instances. You will also explore moving AMIs across regions and automating EBS volume management processes. By the end of this lab, you will have the skills to manage EBS volumes and snapshots, ensuring data durability, scalability, and recovery in real-world applications.
To learn about EBS, check our blog, AWS EFS, EBS ,and S3: Best AWS Storage Option
Lab 2: Create and Mount Elastic File System EFS on EC2
Objective: This lab is designed to provide hands-on experience in creating and managing Amazon Elastic File System (EFS) on EC2 instances.
You will launch two EC2 instances, create an EFS file system, and mount it on both instances for shared access. This lab will walk you through the process of configuring the required security settings, ensuring successful communication between EC2 instances and EFS. Additionally, you will learn how to test the file system by creating files and verifying shared access across instances.
By the end, you will gain the skills to build scalable and reliable storage solutions using Amazon EFS for high-performance cloud applications.
To learn About EFS, check our blog, AWS EFS, EBS and S3: Best AWS Storage Option
Lab 3: Creating Windows File Systems Using Amazon FSx
Objective: This lab is designed to provide hands-on experience in creating and managing Windows File Systems using Amazon FSx for Windows File Server.
You will create an AWS Managed Microsoft Active Directory and deploy an FSx for Windows File System. Additionally, you’ll launch Windows EC2 instances to test the functionality of the FSx file system. Throughout the lab, you will configure RDP access to the EC2 instances, integrate the FSx file system into the Windows environment, and enable Network Discovery and File Sharing.
By the end of this lab, you will have the skills to implement a reliable, high-performance file storage solution using Amazon FSx, providing seamless data access for distributed teams in your organization.
Lab 4: Amazon Kinesis Data Streams – Hands On
Objectives: This lab is designed to provide hands-on experience in creating and managing Amazon Kinesis Data Streams.
You will learn how to set up a Kinesis Data Stream, produce real-time data, and process this data using AWS Lambda. This lab focuses on streaming data ingestion, real-time processing, and scaling to handle increasing data loads.
By the end of this lab, you will be proficient in setting up, configuring, and monitoring a Kinesis Data Stream to support real-time data analytics and processing.
To learn about Amazon Kinesis Data Streams, check our blog, What is AWS Kinesis (Amazon Kinesis Data Streams)?
1.4 Data Transformation, Integrity, and Feature Engineering
Lab 1: Preparing Data for TF-IDF with Jupyter Sagemaker Studio
Objective: Learn how to prepare data for machine learning tasks
This lab guides you through using Spark to process and transform data for TF-IDF feature engineering.
By the end of this lab, you will be able to process large datasets efficiently for advanced machine-learning applications.
Lab 2: Demo: Glue DataBrew
Objective: Learn how to use AWS Glue DataBrew to transform and clean datasets visually.
This lab guides you through using DataBrew to clean, normalize, and transform data without writing any code.
By the end of this lab, you will be able to automate data preparation tasks using DataBrew for improved data quality.
To learn about AWS Glue, check our blog, AWS Glue: Overview, Features, Architecture, Use Cases & Pricing
1.5 Amazon SageMaker and Its Built-In Algorithms
Lab 1: Setting Up Jupyter Notebook Environment in SageMaker Studio
Objective: Set up the Jupyter Notebook environment in SageMaker Studio for model development.
Overview:
This lab will guide you through setting up a Jupyter Notebook environment within SageMaker Studio. You’ll learn how to configure the environment, import necessary libraries, and prepare for building and testing machine learning models. By the end of this lab, you’ll be equipped to use Jupyter Notebooks for data exploration, model training, and evaluation.
To learn about AWS Sagemaker, check our blog, Amazon SageMaker AI For Machine Learning: Overview & Capabilities
Lab 2: Create & Manage SageMaker Studio
Objective: Learn how to create and manage SageMaker Studio for deploying machine learning models.
This lab guides you through deploying and testing pre-built models using SageMaker JumpStart in SageMaker Studio.
By the end of this lab, you will be able to manage SageMaker Studio environments and deploy machine learning models efficiently.
To learn about AWS Sagemaker, check our blog, Amazon SageMaker AI For Machine Learning: Overview & Capabilities
Crack AWS MLA-C01 with 80+ Hands-On Labs – Start Learning Now! Click here
Lab 3: Demo: SageMaker Studio, Canvas, and Data Wrangler
Objective: Learn how to use SageMaker Studio tools for data analysis and model building.
This lab guides you through the functionalities of SageMaker Canvas and Data Wrangler to analyze and prepare data for machine learning.
By the end of this lab, you will be able to utilize SageMaker tools to streamline your machine-learning workflows.
To learn about AWS Sagemaker, check our blog, Amazon SageMaker AI for Machine Learning: Overview & Capabilities
1.6 Generative AI Model Fundamentals
Lab 1: Exploring Transformers with BERT and GPT-2
Objective: Learn how to use Transformer models like BERT and GPT-2 for text generation tasks.
This lab guides you through understanding key concepts such as tokenization and self-attention in Transformer models.
By the end of this lab, you will have a deep understanding of Transformer-based architectures for natural language processing.
1.7 Developing Generative AI Applications with Bedrock
Lab 1: Chat, Text, and Image Models in Bedrock Playground
Objective: Learn how to use foundation models for chat, text, and image generation in Amazon Bedrock Playground.
This lab guides you through experimenting with different models in the Bedrock Playground for generative AI tasks.
By the end of this lab, you will be able to apply foundation models for diverse generative AI applications.
To learn About Amazon Bedrock, check our blog, Amazon Bedrock Explained: A Comprehensive Guide to Generative AI
Lab 2: Building and Querying a RAG System with Amazon Bedrock Knowledge Bases
Objective: Learn how to build and query a Retrieval-Augmented Generation (RAG) system using Amazon Bedrock Knowledge Bases.
This lab guides you through integrating knowledge bases with generative models for enhanced information retrieval.
By the end of this lab, you will be able to build and query effective RAG systems using Bedrock.
To learn About Rag, check our blog, Understanding RAG with LangChain
Lab 3: Building and Testing Guardrails with Amazon Bedrock
Objective: Learn how to build and implement guardrails in Amazon Bedrock to ensure safe and accurate model responses.
This lab guides you through defining and testing guardrails for generative AI models in Amazon Bedrock.
By the end of this lab, you will be able to build secure and reliable generative AI applications using guardrails.
To learn About Amazon Bedrock, check our blog, Amazon Bedrock Explained: A Comprehensive Guide to Generative AI
Lab 4: Build a Bedrock Agent with Action Groups and Guardrails
Objective: Learn how to create a Bedrock agent using action groups, knowledge bases, and guardrails.
This lab guides you through building a Bedrock agent to perform complex tasks with predefined actions and secure model outputs.
By the end of this lab, you will be able to develop agents that combine action groups, knowledge bases, and guardrails for advanced AI tasks.
To learn About Amazon Bedrock, check our blog, Amazon Bedrock Explained: A Comprehensive Guide to Generative AI
1.8 MLOps
Lab 1: Create ECR, Install Docker, Create Image, and Push Image To ECR
Objective: In this lab, you will explore the fundamental concepts of Docker and its role in containerization. You will be guided through the process of installing Docker on your local machine, creating and managing containers, and understanding Docker images and how they support containerized applications. By the end of this lab, you will have a solid understanding of Docker, its installation process, and how to manage containerized applications effectively.
To learn about installing Docker, check our blog, How to Install Docker on Ubuntu: Step-By-Step Guide
Lab 2:Amazon EKS: How to Create a Cluster
Objective: This lab focuses on Amazon Elastic Kubernetes Service (EKS), a managed service that makes it easy to run Kubernetes on AWS.
You will be guided through the process of creating an EKS cluster, deploying applications to the cluster, and scaling them as needed.
By the end of this lab, you will understand how to create and manage EKS clusters and how to deploy containerized applications on them.
To learn about EKS, check our blog, Amazon EKS (Elastic Kubernetes Service): Everything You Should Know
Lab 3: AWS CloudFormation – Hands On
Objective: In this lab, you will work with AWS CloudFormation to automate the deployment and management of AWS resources.
You will learn how to create CloudFormation templates, deploy them, and manage resources using stacks.
By the end of this lab, you will have a clear understanding of how to use CloudFormation to automate AWS infrastructure deployments.
To learn about Cloudformation, check our blog, AWS CloudFormation: Benefits, Working, and Uses
Lab 4: AWS CDK – Hands On
Objective: This lab introduces the AWS Cloud Development Kit (CDK), which allows you to define cloud infrastructure using programming languages.
You will learn how to create and deploy an application using CDK and how to generate and manage CloudFormation stacks programmatically.
By the end of this lab, you will know how to use AWS CDK to write infrastructure as code in a modern, flexible manner.
To learn about AWS CDK, check our blog, Mastering AWS CDK: Key Insights and Best Practices
Lab 5: Amazon EventBridge – Hands On
Objective: In this lab, you will work with Amazon EventBridge, a serverless event bus service that makes it easier to build event-driven applications.
You will learn how to create rules to route events from different AWS services to targets like Lambda functions.
By the end of this lab, you will understand how to use EventBridge to create and manage event-driven workflows across various AWS services.
To learn About AWS EventBridge, check our blog, Amazon Event Bridge: Overview, Use Cases, Benefits, Security
1.9 Security, Identity, and Compliance
Lab 1: Working with AWS IAM
Learn how to manage identity and access control using AWS Identity and Access Management (IAM).
This lab guides you through creating IAM users, roles, and policies to manage access securely in AWS.
By the end of this lab, you will be able to effectively use IAM to control access to your AWS resources.
To learn about Amazon IAM, check our blog, AWS Identity And Access Management (IAM)
Lab 2: Enable Multi-Factor Authentication
Learn how to enhance account security by enabling multi-factor authentication (MFA) in AWS.
This lab guides you through configuring MFA for additional security layers in your AWS environment.
By the end of this lab, you will be able to enable MFA to protect your AWS accounts from unauthorized access.
To learn About Working with Enable Multi-Factor Authentication, check our blog, AWS Multi-Factor Authentication (MFA)
Lab 3 : KMS: Create and Use
Learn how to create and manage encryption keys using AWS Key Management Service (KMS).
This lab guides you through setting up and using KMS to encrypt data and manage secure access to sensitive information.
By the end of this lab, you will be able to create, manage, and use encryption keys to secure data within AWS.
To learn About KMS, check our blog, AWS Key Management Service (AWS KMS) for Data Encryption
Lab 4: AWS Secrets Manager
Learn how to securely store and manage secrets using AWS Secrets Manager.
This lab guides you through storing and retrieving sensitive data like passwords and API keys securely.
By the end of this lab, you will be able to manage secrets securely and access them programmatically in your applications.
To learn About AWS Secrets Manager, check our blog, AWS Secrets Manager
1.10 Management and Governance
Lab 1: Get Started with AWS X-Ray
Learn how to trace and monitor applications using AWS X-Ray.
This lab guides you through configuring AWS X-Ray to trace requests, analyze performance, and troubleshoot issues in distributed applications.
By the end of this lab, you will be able to use AWS X-Ray for detailed monitoring and analysis of your application performance.
To learn About AWS X-Ray, check our blog, AWS X-Ray Overview, Features, and Benefits
Lab 2: Enable CloudTrail and Store Logs in S3
Learn how to enable AWS CloudTrail and store logs in an S3 bucket for auditing and compliance.
This lab guides you through configuring CloudTrail to log API activity and store logs securely in S3.
By the end of this lab, you will be able to audit and track API calls across your AWS account and store logs in S3 for long-term retention.
To learn About CloudTrail, check our Step-by-step blog, CloudWatch vs. CloudTrail: Comparison, Working & Benefits
Lab 3: Setting Up AWS Config
Learn how to set up AWS Config to track configuration changes and ensure compliance across your AWS resources.
This lab guides you through configuring AWS Config to monitor and record changes in resource configurations.
By the end of this lab, you will be able to use AWS Config to maintain compliance and monitor the state of your AWS resources over time.
To learn About Setting Up AWS Config, check our blog, AWS Config: Overview, Benefits, and How to Get Started?
Frequently Asked Questions
Q1: How do these labs align with the AWS Certified Machine Learning Engineer exam objectives?
Ans: Each of these labs has been designed to cover the critical practical skills needed for the AWS Certified Machine Learning Engineer - Associate exam. The labs focus on essential AWS services such as S3 and Glue, which are directly aligned with the exam's objectives. By completing these labs, you’ll gain the hands-on experience necessary to pass the certification and apply your knowledge in real-world machine learning projects.
Q2: What prior knowledge or skills do I need before attempting these labs?
Ans: A basic understanding of AWS services and machine learning concepts is recommended. The labs are designed to guide you through the required steps, making them accessible even if you’re relatively new to some of these services.
Q3: How much time should I allocate to complete all labs?
Ans: Each lab takes around 30-60 minutes, depending on your familiarity with the AWS platform and machine learning concepts. We recommend setting aside about 10-15 hours to complete all labs at your own pace.
Q4: Can these labs be applied to real-world projects outside of the certification?
Ans: Absolutely! The skills you gain from these labs will directly apply to real-world machine learning tasks such as data ingestion, model training, hyperparameter tuning, and model deployment. These are crucial skills for anyone working as a machine learning engineer in the cloud.
Q5: How does hands-on experience from these labs compare to theoretical learning?
Ans: Hands-on experience is invaluable, especially for a certification like AWS Certified Machine Learning Engineer - Associate. Theoretical knowledge is important, but applying that knowledge in a practical context will solidify your understanding and prepare you for real-world scenarios.
Related References
- Join Our Generative AI WhatsApp Community
- Introduction To Amazon SageMaker Built-in Algorithms
- Introduction to Generative AI and Its Mechanisms
- Mastering Generative Adversarial Networks (GANs)
- Generative AI (GenAI) vs Traditional AI vs Machine Learning (ML) vs Deep Learning (DL)
- AWS Certified AI Practitioner (AIF-C01) Certification Exam
- AWS Certified Machine Learning Engineer – Associate (MLA-C01) Exam
Next Task For You
Don’t miss our EXCLUSIVE Free Training on Generative AI on AWS Cloud! This session is perfect for those pursuing the AWS Certified AI Practitioner certification. Explore AI, ML, DL, & Generative AI in this interactive session.
Click the image below to secure your spot!
![Microsoft Agentic AI Business Solutions Architect [AB-100] | K21 Academy](https://test.k21academy.com/wp-content/uploads/2025/11/Microsoft-Agentic-AI-Business-Solutions-Architect-AB-100-Exam-Overview1.png)
