About Us
UF GPA CalculatorUCF GPA CalculatorUC GPA CalculatorASU GPA CalculatorLSAC GPA CalculatorPurdue GPA CalculatorLSU GPA CalculatorRutgers GPA CalculatorOSU GPA CalculatorIU GPA CalculatorUW GPA CalculatorWKU GPA CalculatorTAMU GPA CalculatorUIUC GPA CalculatorUT Austin GPA CalculatorUofSC GPA CalculatorBerkeley GPA CalculatorUNL GPA CalculatorUTK GPA CalculatorUARK GPA CalculatorMizzou GPA CalculatorPenn State GPA CalculatorNYU GPA CalculatorUMich GPA CalculatorFSU GPA CalculatorUCLA GPA CalculatorGeorgia Tech GPA CalculatorUGA GPA CalculatorUNC GPA CalculatorVirginia Tech GPA CalculatorMSU GPA CalculatorUMN GPA CalculatorUofA GPA CalculatorUMD GPA CalculatorCU Boulder GPA CalculatorNC State GPA CalculatorPitt GPA CalculatorUofU GPA CalculatorUOregon GPA CalculatorTemple GPA CalculatorUH GPA CalculatorUW Seattle GPA CalculatorUSC GPA CalculatorNortheastern GPA CalculatorUA GPA CalculatorUIowa GPA CalculatorISU GPA CalculatorBU GPA CalculatorView All Resources →
Praxis44SHRM11CompTIA17HRCI7GED12HiSET6PMP5Nursing29ASVAB7Real Estate8Cloud17Finance7Safety & Trades13IT Security9Fitness3Behavioral Health6Teacher Certification5Engineering1Insurance2Admissions10View All Practice Questions →
Contact
Cloud Practice Questions

AWS Machine Learning Engineer Associate Practice Questions

AWS Certified Machine Learning Engineer - Associate (MLA-C01) — Practice with free sample questions covering data preparation, model development, deployment and orchestration, and monitoring for the AWS Certified Machine Learning Engineer - Associate exam. Each question includes a detailed explanation so you understand the reasoning, not just the answer.

65
Total Questions
130 minutes
Time Limit
720 out of 1000
Passing Score
$150
Registration Fee

Free Sample Questions

Here are 5 free sample questions from our full bank of 490+ AWS Machine Learning Engineer Associatepractice questions. Try them out below — click "Show Answer" to reveal the correct response and explanation.

1

A machine learning engineer is preparing a tabular dataset stored in Amazon S3 for a binary classification model. The dataset contains a categorical 'region' column with 40 unique string values. Which approach is MOST appropriate for preparing this feature before training?

ALeave the column as raw strings and let the training algorithm parse text natively
BRemove the column entirely since categorical features cannot be used in ML models
CConvert the column to a single numeric ID by alphabetically sorting the string values
DApply one-hot encoding (or use AWS Glue DataBrew) to transform the categorical values into numeric feature columns
2

A team is training a gradient boosting model using Amazon SageMaker and notices the model performs very well on training data but poorly on validation data. Which technique would BEST address this issue during model development?

AIncrease the number of boosting rounds to let the model fit more closely
BReduce the size of the training dataset to speed up iteration
CApply regularization and use SageMaker's automatic model tuning to search for hyperparameters that reduce overfitting
DSwitch to a deeper neural network architecture
3

A company wants to deploy an ML model that must return predictions in under 100 milliseconds for a live web application, with traffic that varies significantly throughout the day. Which SageMaker deployment option is MOST appropriate?

ASageMaker Batch Transform, scheduled to run predictions every hour
BA SageMaker real-time endpoint with auto scaling enabled
CSageMaker Processing jobs triggered manually by an operator
DSageMaker Ground Truth for on-demand labeling and inference
4

An ML engineer needs to track model performance over time in production and receive an automatic alert if data drift causes prediction quality to degrade. Which AWS service or feature is BEST suited for this requirement?

AAmazon SageMaker Model Monitor integrated with Amazon CloudWatch alarms
BAWS CloudTrail logging of API calls made to the endpoint
CAmazon S3 versioning on the training dataset bucket
DAWS Identity and Access Management (IAM) access analyzer
5

A retail company wants to build a recommendation system but has a small internal team with limited deep learning expertise. They want a managed solution that handles the underlying model training and infrastructure. Which AWS service BEST fits this use case?

AAmazon Personalize
BAmazon EC2 instances with manually installed TensorFlow
CAWS Lambda functions running custom Python scripts
DAmazon Redshift Spectrum

Get the Full AWS Machine Learning Engineer Associate Question Bank — 490+ Practice Questions

You just saw 5 sample questions. We have a complete bank of 490+ AWS Machine Learning Engineer Associatepractice questions with detailed answers and explanations ready for you. Fill out the form below and we'll send you the full question bank — completely free.

We'll send the full question bank to this email.

We won't spam you. Just a quick follow-up if needed.

All fields are required.

About the AWS Machine Learning Engineer Associate

Format & Structure

Total Questions
65
Time Limit
130 minutes
Format
Multiple choice and multiple response

Scoring & Cost

Passing Score
720 out of 1000
Registration Fee
$150

Related Practice Questions

AWS Cloud Practitioner Practice Questions

Start Practicing

AWS Solutions Architect Associate Practice Questions

Start Practicing

AWS Developer Associate Practice Questions

Start Practicing

AWS Security Specialty Practice Questions

Start Practicing

Frequently Asked Questions

What is the AWS Certified Machine Learning Engineer - Associate certification?

It's an AWS certification (exam code MLA-C01) that validates your ability to build, train, deploy, and monitor machine learning solutions on AWS. Unlike the older Machine Learning Specialty exam, this Associate-level credential focuses more on the engineering and operational side -- data pipelines, deployment workflows, and production monitoring -- rather than deep statistical theory.

How many questions are on the MLA-C01 exam, and how long do I get?

The exam has 65 questions and you're given 130 minutes to complete it. Questions come in two formats: standard multiple choice (one correct answer) and multiple response (select two or more correct answers from a longer list), so read each question carefully before answering.

What score do I need to pass the AWS Machine Learning Engineer Associate exam?

AWS scores this exam on a scale of 100 to 1000, and you need a minimum of 720 to pass. That works out to roughly 72% of the scored content, though AWS doesn't publish an exact question-by-question breakdown since scoring is scaled across different exam forms.

How much does the MLA-C01 exam cost?

The exam costs $150 USD, which is standard pricing for AWS Associate-level certifications. AWS occasionally offers 50% discount vouchers for exam retakes or through certain training programs, so it's worth checking your AWS Certification account before you register.

What are the main domains covered on the exam?

The exam is organized around four domains: data preparation for machine learning, ML model development, deployment and orchestration of ML workflows, and monitoring, maintenance, and security of ML solutions. Data preparation and model development together make up over half the scored content, so don't skimp on those areas while you study.

Do I need to know how to code for this exam?

You don't need to be a professional software engineer, but you should be comfortable reading Python-style pseudocode and understand how services like SageMaker, Glue, and Lambda fit together in an ML pipeline. The exam tests applied knowledge of AWS ML tooling more than it tests raw coding ability.

How is this different from the AWS Machine Learning Specialty exam?

The Specialty exam leans heavily into ML theory, statistics, and algorithm selection, while the Associate-level MLA-C01 focuses more on practical engineering tasks -- building pipelines, deploying models, and keeping them running reliably in production. Many candidates find the Associate exam more approachable if their background is closer to DevOps or data engineering than data science.

How should I prepare for the AWS Machine Learning Engineer Associate exam?

Start with AWS's own exam guide to understand the domain weightings, then get hands-on with SageMaker -- build a small pipeline that ingests data, trains a model, deploys an endpoint, and sets up Model Monitor. Pairing that hands-on practice with scenario-based practice questions, like the ones above, helps you get comfortable with how AWS phrases its exam scenarios before test day.

Get 490+ AWS Machine Learning Engineer Associate Practice Questions

Don't settle for just 5 sample questions. Request the full question bank and start preparing with confidence.

Get Started