The AWS Certified Machine Learning Engineer – Associate (MLA-C02) exam introduces updated skills for professionals working with machine learning and generative AI solutions on AWS. The updated certification expands the previous MLA-C01 scope with foundation models, Amazon Bedrock, Retrieval-Augmented Generation (RAG), and agentic AI workflows.
If you are preparing for MLA-C02, understanding the latest exam objectives and practicing scenario-based questions can help you prepare more effectively. This guide explains what's new, what existing MLA-C01 candidates should study, and how to use updated practice materials.
MLA-C02 Exam at a Glance: What's New?
MLA-C02 expands traditional machine learning engineering to include modern generative AI capabilities. The four-domain structure remains unchanged, but existing tasks and skills have been updated.
| Exam Domain | Weightage | What's New in MLA-C02? |
|---|
| Data Preparation for ML and AI | 28% | Vector databases, embeddings, RAG document preparation, data masking, and foundation model data preparation |
| ML Model and Foundation Model Development | 24% | Foundation model selection, fine-tuning, RAG evaluation, LLM metrics, and responsible AI |
| Deployment and Orchestration of ML and AI Workflows | 24% | Foundation model deployment, Bedrock Knowledge Bases, AI agents, retrieval workflows, and CI/CD |
| Operating, Monitoring, and Securing ML and AI Solutions | 24% | Agent observability, token and embedding cost monitoring, Bedrock Guardrails, and AI security |
Key MLA-C02 Updates
- Generative AI implementation: Build and deploy generative AI solutions using Amazon Bedrock and RAG architectures.
- Foundation models and LLMs: Understand model selection, customization, fine-tuning, and operationalization.
- Agentic AI: Learn how to orchestrate AI agents and complex workflows.
- Amazon Bedrock: Study expanded capabilities for foundation model applications.
- Responsible AI: Understand responsible AI practices across traditional machine learning and generative AI.
MLA-C02 Exam Format and Important Details
| Exam Detail | MLA-C02 Beta |
|---|
| Certification | AWS Certified Machine Learning Engineer – Associate |
| Exam Code | MLA-C02 |
| Level | Associate |
| Exam Duration | 170 minutes |
| Number of Questions | 85 |
| Exam Price | USD 75 (beta pricing) |
| Language | English |
| Delivery | Pearson VUE test center or online proctored |
| Certification Validity | 3 years |
Important: The 85-question, 170-minute format applies to the beta exam. AWS's standard exam guide describes 50 scored and 15 unscored questions. Beta exams include additional evaluation questions and use a different result process.
What MLA-C01 Candidates Should Add
If you have already prepared for MLA-C01, much of your traditional ML knowledge remains useful. However, you should add the following MLA-C02 topics to your preparation:
- Learn Amazon Bedrock, foundation models, and LLM capabilities.
- Understand RAG architecture, embeddings, vector databases, and document chunking.
- Practice foundation model selection, customization, and fine-tuning concepts.
- Study AI agent orchestration, tool integration, and agent workflows.
- Review Bedrock Knowledge Bases and retrieval strategies.
- Understand generative AI evaluation, hallucinations, and responsible AI.
- Learn token usage, embedding costs, observability, and Bedrock Guardrails.
How Clearcat.net Can Support MLA-C02 Preparation
MLA-C02 preparation requires more than memorizing AWS service names. Candidates should understand how different services work together in real-world machine learning and generative AI scenarios.
Clearcat.net provides exam preparation materials designed to help candidates review important topics and strengthen their understanding.
With updated MLA-C02 preparation materials, you can:
- Review questions covering all four official exam domains.
- Practice traditional machine learning and generative AI scenarios.
- Understand Amazon Bedrock, RAG, foundation models, and AI agents.
- Identify weak areas through practice assessments.
- Read explanations to understand why an answer is correct.
- Improve your confidence with exam-style practice.
Explore MLA-C02 preparation materials on Clearcat.net and use practice questions alongside official AWS documentation and hands-on learning.
Try 5 Free MLA-C02 Demo Questions
Test your understanding with these five original practice questions based on MLA-C02 topics. These questions are for preparation purposes and are not actual AWS exam questions.
Question 1: RAG Document Preparation
A company wants to build a Retrieval-Augmented Generation (RAG) application using thousands of internal documents. The documents must be divided into smaller sections before generating embeddings and storing them in a vector database.
Which technique should the ML engineer use?
A. Model quantization
B. Document chunking
C. Data encryption
D. Hyperparameter tuning
Correct Answer: B. Document chunking
Explanation: Document chunking divides large documents into smaller sections. These sections can be converted into embeddings and indexed for retrieval. This allows a foundation model to receive relevant context when answering user questions.
Question 2: Foundation Model Integration
A development team wants to build a generative AI application using foundation models without managing the underlying model infrastructure. The team also wants access to different foundation models through a managed AWS service.
Which service should the team use?
A. Amazon SageMaker Data Wrangler
B. Amazon Bedrock
C. AWS CloudTrail
D. Amazon Route 53
Correct Answer: B. Amazon Bedrock
Explanation: Amazon Bedrock provides access to foundation models through a managed service. Developers can use it to build generative AI applications, implement model invocation, and integrate foundation models into application workflows.
Question 3: Model Evaluation
An ML engineer is evaluating a classification model. The business wants to measure how many predicted positive cases were actually positive.
Which evaluation metric should the engineer use?
A. Recall
B. Precision
C. Mean Squared Error
D. Root Mean Squared Error
Correct Answer: B. Precision
Explanation: Precision measures the proportion of predicted positive cases that are actually positive. It is calculated by dividing true positives by the total number of predicted positives.
Question 4: AI Application Security
A company is deploying a generative AI application using Amazon Bedrock. The application must filter harmful content and help protect sensitive information in model inputs and outputs.
Which capability should the company consider?
A. Amazon Bedrock Guardrails
B. Amazon S3 Lifecycle
C. AWS CloudFormation
D. Amazon EC2 Auto Scaling
Correct Answer: A. Amazon Bedrock Guardrails
Explanation: Amazon Bedrock Guardrails provides configurable safeguards for generative AI applications. It supports content filtering, sensitive information protection, and other responsible AI controls.
Question 5: ML Workflow Orchestration
An ML engineering team wants to coordinate multiple workflow steps, including data processing, model training, and deployment. The team needs a service that can manage workflow execution across different AWS services.
Which AWS service is most appropriate?
A. Amazon CloudFront
B. AWS Step Functions
C. Amazon Polly
D. Amazon Route 53
Correct Answer: B. AWS Step Functions
Explanation: AWS Step Functions coordinates multiple workflow steps and AWS services. It can be used to orchestrate automated ML and AI processes, including data processing and deployment workflows.
Final Preparation Tips for MLA-C02
Before taking the MLA-C02 exam, make sure you:
- Review all four official exam domains.
Understand the differences between MLA-C01 and MLA-C02.
- Practice Amazon Bedrock and foundation model scenarios.
- Revise RAG, embeddings, and vector database concepts.
- Understand AI agent workflows and deployment.
- Study model monitoring, security, and cost optimization.
- Complete practice assessments and review incorrect answers.
Start your MLA-C02 preparation with updated practice materials from Clearcat.net and strengthen your machine learning and generative AI knowledge.
Clearcat.net – First Attempt Success!
Written By:CLEARCATNET TEAM
Published on: 14/11/2026
Checkout more latest blogs here -blogs