MLA-C01 Practice Tests

AWS Certified Machine Learning Engineer Associate

Prepare for MLA-C01 with practice questions aligned to the official AWS Machine Learning Engineer Associate domains. Cover data preparation, model development, ML workflow deployment, monitoring, maintenance, and security.

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Duration

130 minutes

Questions

65 questions (50 scored, 15 unscored)

Cost

$150 USD

Where to register

Amazon Web Services

Issued by Amazon Web Services. Delivered via Pearson VUE or online proctored exam. AWS recommends at least 1 year using SageMaker and other AWS services for ML engineering.

01·Overview

Certification overview

The format, prerequisites, and what to expect on exam day.

Exam details

Exam Code
MLA-C01
Duration
130 minutes
Questions
65 questions (50 scored, 15 unscored)
Format
Multiple choice and multiple response
Passing Score
720/1000
Cost
$150 USD
Validity
3 years

Prerequisites

At least 1 year using SageMaker and AWS services for ML engineering
Experience in a related role such as backend developer, DevOps developer, data engineer, or data scientist
Basic understanding of common ML algorithms and data engineering fundamentals
Familiarity with CI/CD, infrastructure as code, monitoring, and AWS security best practices

02·Domains

Exam domains

Topics on the official blueprint, with their relative weight.

Data Preparation for Machine Learning

28%

Ingest and store data
Transform data and perform feature engineering
Ensure data integrity and prepare data for modeling

ML Model Development

26%

Choose a modeling approach
Train and refine models
Analyze model performance

Deployment and Orchestration of ML Workflows

22%

Choose deployment infrastructure
Script and provision infrastructure
Set up CI/CD pipelines for ML workflows

ML Solution Monitoring, Maintenance, and Security

24%

Monitor model performance and data quality
Optimize infrastructure and costs
Secure AWS resources

03·Key topics

What you actually study

Service families and concept clusters that show up across questions.

SageMaker Workflows

SageMaker training and endpoints
SageMaker Pipelines
Feature Store
Data Wrangler
Model Monitor

Data Preparation

S3
Glue
EMR
Athena
feature engineering
data validation

Deployment

ECR
Step Functions
EventBridge
CodePipeline
autoscaling
endpoint choices

Monitoring and Security

CloudWatch
SageMaker Clarify
IAM
KMS
VPC controls
cost optimization

04·Study tips

How to actually pass it

Practical strategies for the weeks before, and the morning of.

Preparation strategy

Practice connecting data preparation choices to downstream model quality.
Review SageMaker training, tuning, model registry, pipelines, endpoints, monitoring, and Clarify.
Drill CI/CD and orchestration scenarios for repeatable ML workflows.
Study IAM, KMS, VPC, logging, and monitoring controls as part of every ML architecture.

Exam day

Classify the question by ML lifecycle stage before selecting the AWS service.
Favor repeatable pipeline and monitoring patterns over one-time manual actions.
Read carefully for cost, latency, drift, security, and deployment constraints.
Use the domain weights to keep data preparation and model development high priority.

Turn AWS ML engineering knowledge into exam readiness.

Practice data, model, pipeline, deployment, monitoring, and security scenarios. Start free, no card required.

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