Prepare for AWS MLA-C01 with K21 Academy: Associate ML Engineer Exam

In June 2024, the AWS Training and Certification team unveiled a new addition to their extensive certification portfolio: the AWS Certified Machine Learning Engineer – Associate (MLA-C01).

In this blog, we’ll explore everything you need to know about the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam. Whether you’re an aspiring machine learning engineer or a seasoned professional looking to solidify your skills in the AWS ecosystem, this certification can help you advance your career. We’ll cover the exam structure, key domains, the skills you need, and how to best prepare for success.

By the end of this blog, you’ll have a clear understanding of what to expect from the exam and how it can boost your credentials in the growing field of AI and machine learning.

This blog will cover everything you need to know about the AWS MLA-C01:

1. What is the AWS Certified Machine Learning Engineer – Associate MLA-C01 Exam?
2. Exam Overview
3. Topics Included in Exam:
4. AWS [MLA-C01] Hands-on guides
5. Difference Between Existing MLS-C01 AWS Specialty Certification and New MLA-C01 Associate Exam
6. Why Pursue AWS Certified Machine Learning Engineer – Associate Certification?
7. Career Paths and Opportunities
8. Exam Results for AWS Certified Machine Learning Engineer – Associate (MLA-C01)
9. Conclusion

What is the AWS Certified Machine Learning Engineer – Associate MLA-C01 Exam?

The AWS Certified Machine Learning Engineer – Associate (MLA-C01) Exam is a specialized certification offered by Amazon Web Services (AWS). It is tailored for professionals seeking to validate their skills in machine learning (ML) on the AWS platform. This certification affirms an individual’s ability to effectively design, implement, deploy, and maintain ML solutions using AWS services.

Related Readings: K21 Academy: Your Ultimate Guide to AWS AI/ML Certification in 2025

Related Readings: AWS Certification Path: A Step-by-Step 2025 Guide

MLA-C01 Exam Overview:

Topics Included in MLA-C01 Exam:

The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam focuses on the following 4 domains:

1. Domain 1: Data Preparation for Machine Learning (ML) (28%)
2. Domain 2: ML Model Development (26%)
3. Domain 3: Deployment and Orchestration of ML Workflows (22%)
4. Domain 4: ML Solution Monitoring, Maintenance, and Security (24%)

Domain 1: Data Preparation for Machine Learning (28%)

1. Ingest and store data:

• Data formats (e.g., Apache Parquet, JSON, CSV, ORC, Avro, RecordIO)
• AWS core data sources (e.g., Amazon S3, Amazon EFS, Amazon FSx)
• AWS streaming data sources (e.g., Amazon Kinesis, Apache Flink, Apache Kafka)
• Storage options and tradeoffs in AWS (e.g., Amazon S3, EBS, DynamoDB)

Related Readings: AWS S3 Bucket | Amazon Simple Storage Service Bucket

2. Transform data and perform feature engineering:

• Data cleaning and transformation techniques (e.g., outlier detection, deduplication)
• Feature engineering techniques (e.g., scaling, standardization, binning)
• Encoding techniques (e.g., one-hot encoding, tokenization)
• Tools for data transformation (e.g., AWS Glue, SageMaker Data Wrangler)
• Data annotation and labeling (e.g., SageMaker Ground Truth)

3. Ensure data integrity and prepare data for modeling:

• Pre-training bias metrics for numeric, text, and image data (e.g., class imbalance)
• Techniques to address bias (e.g., resampling, synthetic data generation)
• Data encryption, anonymization, and compliance requirements (e.g., PII, PHI)
• Validating data quality (e.g., using AWS Glue DataBrew)

Domain 2: ML Model Development (26%)

1. Choose a modeling approach:

• ML algorithms for solving business problems
• AWS AI services (e.g., Amazon Rekognition, Amazon Translate, Amazon Bedrock)
• SageMaker built-in algorithms and their application
• Interpretability and model selection based on business needs

2. Train and refine models:

• Elements in the training process (e.g., epochs, batch size, hyperparameter tuning)
• Methods to improve performance (e.g., regularization, dropout, tuning)
• Preventing overfitting, underfitting, and catastrophic forgetting
• SageMaker algorithms and ML libraries (e.g., TensorFlow, PyTorch)
• Version management for model training and experimentation

3. Analyze model performance:

• Evaluation metrics (e.g., F1 score, RMSE, ROC-AUC)
• Methods to identify overfitting and underfitting
• Using SageMaker Clarify to detect bias and gain insights
• Debugging model convergence using SageMaker Model Debugger

Related Readings: AWS AI/ML: Hands-On Labs & Projects for High-Paying Careers in 2025

Domain 3: Deployment and Orchestration of ML Workflows (22%)

1. Select deployment infrastructure based on architecture and requirements:

• Best practices for deployment (e.g., versioning, rollback strategies)
• AWS deployment services (e.g., SageMaker endpoints, batch inference)
• Model deployment targets (e.g., SageMaker, Kubernetes, ECS, EKS, Lambda)
• Optimizing models on edge devices (e.g., SageMaker Neo)

2. Create and script infrastructure:

• Infrastructure as code (e.g., AWS CloudFormation, AWS CDK)
• Provisioning compute resources for training and inference
• Auto-scaling policies for SageMaker endpoints based on demand

3. Use automated orchestration tools to set up CI/CD pipelines:

• AWS CI/CD tools (e.g., CodePipeline, CodeBuild, CodeDeploy)
• Automating training and inference jobs (e.g., using EventBridge, SageMaker Pipelines)
• Integrating CI/CD pipelines with AWS services for model deployment and retraining

Related Reading: Case Study: How To Deploy Web App From S3 Bucket To EC2 Instance on AWS Using CodePipeline

Domain 4: ML Solution Monitoring, Maintenance, and Security (24%)

1. Monitor model inference:

• Model monitoring tools (e.g., SageMaker Model Monitor)
• Detecting drift in models and data
• Monitoring anomalies in inference using SageMaker Clarify

2. Monitor and optimize infrastructure and costs:

• Key performance metrics for ML infrastructure (e.g., utilization, scalability)
• Troubleshooting latency and performance (e.g., using AWS CloudWatch)
• AWS cost optimization tools (e.g., AWS Cost Explorer, Trusted Advisor)

Related Reading: CloudWatch vs. CloudTrail: Comparison, Working & Benefits

3. Secure AWS resources:

• Configuring IAM roles, policies, and access controls
• Securing ML resources and pipelines (e.g., SageMaker Role Manager, VPC configuration)
• Auditing and logging ML systems for compliance and security

Checkout: AWS Certified Machine Learning Engineer – Associate

AWS MLA-C01 Hands-on Guides

For AWS ML we have 32 Step-by-step Activity Guides for you to practice and have a clear knowledge of concepts both theoretically and practically

Difference Between Existing MLS-C01 AWS Specialty Certification and New MLA-C01 Associate Exam

Related Reading: AWS Certified Machine Learning- Specialty Certification

Why Pursue AWS Certified Machine Learning Engineer – Associate Certification?

Achieving the AWS Certified Machine Learning Engineer – Associate certification validates your proficiency in designing, deploying, and optimizing machine learning solutions using AWS services like Amazon SageMaker. This credential is crucial for showcasing your ability to handle complex ML tasks, ensuring data accuracy, optimizing model performance, and implementing secure ML workflows. It enhances your credibility in roles such as Data Engineering, MLOps, and AI development, opening doors to new career opportunities and higher earning potential in the competitive landscape of cloud computing and artificial intelligence.

Career Paths and Opportunities

Earning the AWS Certified Machine Learning Engineer – Associate MLA-C01 certification opens doors to a variety of rewarding career paths in the rapidly growing fields of machine learning and cloud computing. Professionals holding this credential are well-equipped for roles such as:

• Machine Learning Engineer: Designing and deploying ML models, optimizing performance, and integrating solutions within AWS infrastructure.
• Data Engineer: Managing data pipelines, transforming data for ML applications, and ensuring data quality and availability.
• MLOps Engineer: Implementing best practices for deploying and monitoring ML models in production environments, leveraging AWS tools for automation and scalability.
• Data Scientist: Applying statistical models and ML algorithms to derive insights from data, using AWS services for scalable analytics and experimentation.
• AI Specialist: Developing AI-driven applications, leveraging AWS ML capabilities to enhance customer experiences and operational efficiency.

Related Readings: Top 10 AI Job Roles in 2025 | K21Academy

These roles span industries such as healthcare, finance, and retail, where AWS’s robust machine-learning infrastructure is increasingly adopted. With the demand for skilled professionals in cloud-based ML solutions on the rise, this certification validates your expertise and positions you for career advancement and leadership opportunities in cutting-edge technology domains.

Related Readings: Amazon SageMaker Built-in Algorithms – Introduction

Exam Results for AWS Certified Machine Learning Engineer – Associate (MLA-C01)

1. Pass or Fail Designation: The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam results are provided as either pass or fail based on your overall performance.
2. Scaled Score: Your exam score is reported as a scaled score between 100 and 1,000, with the minimum passing score set at 720. This ensures consistency across different exam forms, adjusting for variations in difficulty.
3. Compensatory Scoring Model: You do not need to pass each section individually. Instead, the exam uses a compensatory scoring model, meaning only the overall exam score matters for passing.
4. Section-Level Performance: Your score report may include a breakdown of your performance by section. Each section is weighted differently, and some sections may contain more questions than others.
5. Feedback on Strengths and Weaknesses: The section-level feedback offers general information about your strengths and weaknesses. However, it is meant to provide broad insights and should be interpreted cautiously.
6. Certification Standards: The passing score is set by AWS professionals, ensuring the exam aligns with certification industry best practices and guidelines.

Conclusion

The AWS Certified Machine Learning Engineer – Associate (MLA-C01) Exam offers professionals a vital opportunity to validate their AWS machine learning skills. Tailored for associate-level ML engineers, it covers essential topics like data preparation, model training, and deployment on AWS. Prepare thoroughly with official resources and practical experience to excel in roles spanning data engineering, MLOps, and more.

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