AWS · MLA-C01
AWS Certified Machine Learning Engineer - Associate (MLA-C01) Practice Test
Validates ability to build, operationalize, deploy, and maintain machine learning solutions and pipelines using AWS Cloud services.
Exam Details
Questions
582
Duration
130 minutes
Passing Score
720/1000
Difficulty
AssociateLast Updated
Jan 2026
AWS Certified Machine Learning Engineer - Associate (MLA-C01) Practice Exam Preparation
Use this MLA-C01 practice exam to prepare for AWS Certified Machine Learning Engineer - Associate (MLA-C01) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 582 questions for AWS MLA-C01, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to patterns in your missed answers. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Data Preparation for Machine Learning28%
ML Model Development26%
Deployment and Orchestration of ML Workflows22%
ML Solution Monitoring, Maintenance, and Security24%
Exam Overview
The AWS Certified Machine Learning Engineer – Associate (MLA-C01) validates a candidate's ability to build, operationalize, deploy, and maintain machine learning solutions and pipelines on AWS. Launched in October 2024, this role-based certification fills a critical gap in the AWS certification portfolio, targeting the engineer who bridges the gap between data science prototypes and production-ready ML systems. The exam tests practical knowledge of the full ML engineering lifecycle: data ingestion and preparation, model training and tuning, deployment and orchestration, and ongoing monitoring and security.
The certification covers a broad set of AWS services centered on Amazon SageMaker, alongside data storage and processing services, CI/CD and orchestration tools, monitoring and logging platforms, and security controls. Notably, the exam does not test deep domain expertise in NLP or computer vision, nor does it cover end-to-end ML solution architecture—those concerns fall to the AWS Certified Machine Learning – Specialty exam. MLA-C01 is specifically scoped to the operational and engineering tasks an ML engineer performs day-to-day in a cloud environment.
Who Should Take This Exam
This certification is designed for ML engineers, MLOps engineers, DevOps engineers, data engineers, and backend software developers who work with machine learning systems on AWS. The ideal candidate has at least one year of hands-on experience with Amazon SageMaker and related AWS ML services, combined with at least one year of experience in a related engineering role. Data scientists looking to strengthen their deployment and operationalization skills will also benefit.
Candidates should be comfortable with software engineering best practices such as modular code design, debugging, and deployment, as well as CI/CD pipelines, Infrastructure as Code (IaC), and version control. This is not an entry-level credential—it assumes working knowledge of ML concepts, data engineering fundamentals, and cloud infrastructure provisioning.
Prerequisites
There are no mandatory prerequisites to sit for the MLA-C01 exam; AWS does not require any prior certification. However, AWS recommends at least one year of hands-on experience using Amazon SageMaker and other AWS ML engineering services, as well as one year of experience in a related role such as backend development, DevOps, or data engineering.
Recommended foundational knowledge includes: common ML algorithms and their use cases, data engineering concepts (formats, ingestion pipelines, transformation), data querying and transformation skills, CI/CD pipeline design and orchestration, cloud resource provisioning and monitoring, and AWS security fundamentals including identity management and encryption. Candidates new to AWS may benefit from first earning the AWS Certified Cloud Practitioner or AWS Certified AI Practitioner, though neither is required.
Exam Format
The MLA-C01 exam consists of 65 total questions—50 scored and 15 unscored. The unscored questions are used by AWS to evaluate potential future exam content and are not identified during the exam. The time limit is 130 minutes. The exam is delivered through Pearson VUE at a testing center or via online proctoring. It is available in English, Japanese, Korean, and Simplified Chinese. The exam fee is $150 USD.
Four question types are used: multiple choice (one correct answer out of four), multiple response (two or more correct answers out of five or more options, requiring all correct selections for credit), ordering (arrange 3–5 steps in the correct sequence), and matching (match 3–7 response pairs). Scores are reported on a scaled range of 100–1,000, with a passing score of 720. The exam uses a compensatory scoring model, meaning no per-domain minimum is required. Unanswered questions are scored as incorrect; there is no penalty for guessing. The certification is valid for three years.
Skills Measured
- 1.Domain 1: Data Preparation for Machine Learning (28%) — Covers ingesting data from AWS sources, transforming and validating datasets, feature engineering, handling class imbalance, splitting datasets, and using AWS services such as Amazon S3, AWS Glue, Amazon Athena, and SageMaker Data Wrangler to prepare data for ML modeling.
- 2.Domain 2: ML Model Development (26%) — Covers selecting appropriate ML algorithms and frameworks, training models using Amazon SageMaker, tuning hyperparameters with SageMaker Automatic Model Tuning, analyzing model performance metrics, managing model versions with the SageMaker Model Registry, and applying responsible AI practices.
- 3.Domain 3: Deployment and Orchestration of ML Workflows (22%) — Covers selecting deployment infrastructure and endpoint types (real-time, batch, asynchronous), provisioning and managing compute resources, configuring auto scaling, building and automating CI/CD pipelines for ML using services like AWS CodePipeline, AWS Step Functions, and Amazon SageMaker Pipelines.
- 4.Domain 4: ML Solution Monitoring, Maintenance, and Security (24%) — Covers monitoring model performance and data drift using SageMaker Model Monitor, setting up logging and alerting with Amazon CloudWatch, retraining triggers, applying security controls such as IAM policies, VPC configurations, encryption at rest and in transit, and ensuring compliance and auditability of ML systems.
Study Tips
- Start with the official AWS Exam Guide (available on the AWS Certification page) to understand the four domains and their weights—use it to identify your weakest areas and prioritize study time accordingly.
- Complete the official 'Exam Prep Enhanced Course: AWS Certified Machine Learning Engineer – Associate' on AWS Skill Builder, which includes exam-style questions with instructor walkthroughs and maps directly to the four exam domains.
- Get hands-on with Amazon SageMaker end-to-end: practice building training jobs, creating endpoints, configuring SageMaker Pipelines, and setting up Model Monitor. The exam heavily emphasizes operational SageMaker knowledge over theory.
- Use AWS Builder Labs and AWS Cloud Quest (available on AWS Skill Builder) to practice in real AWS environments without incurring account costs—focus on labs covering SageMaker, Glue, Step Functions, and CloudWatch.
- Take the official AWS practice exam (available on AWS Skill Builder) under timed conditions to acclimate to the ordering and matching question formats, which are less familiar than standard multiple choice.
- Study the in-scope AWS services list published in the official exam guide—pay particular attention to SageMaker features (Data Wrangler, Feature Store, Model Registry, Pipelines, Model Monitor) and supporting services like AWS Glue, Amazon Athena, AWS CodePipeline, and Amazon CloudWatch.
- Review the out-of-scope topics in the official exam guide to avoid over-studying—deep NLP/computer vision domain work, full solution architecture, and model quantization are explicitly not tested, so focus your energy on MLOps and deployment workflows.
Career Benefits
The MLA-C01 certification targets some of the fastest-growing roles in technology. The World Economic Forum's Future of Jobs Report projects demand for AI and ML Specialists to grow by more than 80% by 2030, and the U.S. Bureau of Labor Statistics forecasts 34% growth for data scientists between 2024 and 2034. The certification validates skills directly applicable to ML Engineer, MLOps Engineer, and AI/ML Platform Engineer roles. While the credential launched in late 2024 and direct salary correlation data is still emerging, related benchmarks are strong: ZipRecruiter places AWS Machine Learning Engineers at an average of approximately $145,000–$146,000 annually, and Payscale data shows that ML engineers with AWS skills earn roughly $5,600 more than peers without them. AWS Certified Machine Learning – Specialty holders average $213,000 according to Skillsoft's 2024 IT Skills and Salary Report, indicating the premium that AWS ML credentials command.
Within the AWS certification ecosystem, MLA-C01 sits between the AWS Certified AI Practitioner (foundational) and the AWS Certified Machine Learning – Specialty (advanced), making it a natural stepping stone for engineers building a structured AWS ML career path. It complements the AWS Certified Data Engineer – Associate and AWS Certified Solutions Architect – Associate, and is increasingly listed as a preferred qualification in ML engineering job postings on major platforms.
Sample Questions
5 sample questions with answers and explanations. Start a practice session to test yourself across all 582 questions.
Preview — answers shown1. A quantitative finance firm has developed a novel, proprietary stock prediction algorithm written entirely in C++. They want to run this algorithm on SageMaker to leverage its managed training infrastructure and distributed training capabilities. How must they package their custom C++ code to make it compatible with SageMaker training jobs?
AUpload the C++ source code directly to an S3 bucket.
BCompile the C++ code into an executable, place it inside a Docker container that conforms to SageMaker's specifications, and upload the container image to Amazon ECR.
CRewrite the algorithm in Python using the SageMaker SDK.
DUse a SageMaker built-in algorithm and specify the C++ file as a hyperparameter.
Explanation
The correct method is to containerize the application using Docker. Here's why: SageMaker's 'Bring Your Own Algorithm' functionality is based on Docker containers. To use a custom algorithm written in any language, you must package it in a Docker container. This container needs to include all necessary libraries, the compiled executable, and a script that tells SageMaker how to run the training. This container is then pushed to Amazon ECR, from where SageMaker can pull it to run the training job. Why the other options are incorrect: - A: SageMaker would not know how to compile or execute raw C++ source code from S3. - B: Built-in algorithms are pre-packaged and cannot be extended with custom C++ code. - D: Rewriting the algorithm is a possible but often undesirable and time-consuming alternative; containerization allows them to use their existing code.
2. A team needs to parameterize their SageMaker Pipeline. They want to use the same pipeline definition for training on different datasets (by passing in a different S3 URI) and for trying different hyperparameters (by passing in a different learning rate). How are these runtime values defined and passed to a pipeline execution?
ABy using a different IAM role for each execution, with the parameters defined in the role's policy.
BBy storing the parameters in an S3 object and reading it at the start of the pipeline.
CBy creating a `ParameterString` or `ParameterFloat` in the pipeline definition and then providing values for them when calling `start_pipeline_execution`.
DBy hardcoding the values as environment variables in the pipeline definition.
Explanation
The correct method is to use Parameter objects in the pipeline definition. Here's why: SageMaker Pipelines are designed to be parameterized for reusability. You define placeholders in your pipeline script using classes like `ParameterString`, `ParameterInteger`, `ParameterFloat`, etc. These act as variables. When you initiate a pipeline run, you can then pass a dictionary of concrete values for these parameters. This allows the same pipeline definition to be reused for different experiments or projects simply by changing the input parameters for each run. Why the other options are incorrect: - A: Hardcoding values makes the pipeline inflexible and not reusable. - C: IAM roles are for permissions, not for passing workflow parameters. - D: This is a custom workaround for a feature that is natively and more cleanly supported by the service itself.
3. A research team has a model that takes up to 10 minutes to process a single large medical image. They need an on-demand endpoint that can accept the large image file and process it without the client application timing out. Which SageMaker deployment option is designed for this use case?
ABatch Transform
BMulti-Model Endpoint
CAsynchronous Inference Endpoint
DReal-time Endpoint
Explanation
An Asynchronous Inference Endpoint is specifically designed for workloads with large payloads and long processing times. The client sends a request and immediately gets an acknowledgment with a location to poll for the result. SageMaker processes the request in the background and places the output in S3, avoiding client timeouts. This is ideal for the 10-minute processing time described. Why the others are wrong: - A Real-time Endpoint would time out after about 60 seconds. - Batch Transform is for processing entire datasets, not single on-demand requests. - A Multi-Model Endpoint is for hosting many models, but it still follows the real-time request/response pattern and would time out.
4. An MLOps team is preparing to deploy a new version of their model. Before routing any live traffic to it, they want to validate its performance by sending a copy of the production traffic to the new model and comparing its predictions to the current model's predictions. This must be done with zero impact on end-users. Which deployment strategy should they use?
ACanary Deployment
BBlue/Green Deployment
CA/B Testing
DShadow Deployment
Explanation
A Shadow Deployment is the correct strategy. In this pattern, the new model (the 'shadow') runs in parallel with the production model and receives a copy of the live traffic. However, its responses are not sent back to the user; they are simply logged for analysis. This allows the team to test the new model under a real-world load and compare its behavior to the production model without any risk or impact to the customer experience. Why the others are wrong: - Blue/Green, Canary, and A/B Testing all involve serving responses from the new model to at least a subset of users, which has a direct customer impact.
5. An e-commerce platform stores its sales data in an S3 data lake, with a file structure like `s3://bucket/sales_data_2023_10_26.csv`. Queries in Amazon Athena that filter by a date range are very slow and expensive. How should the S3 object keys be restructured to significantly improve query performance and reduce cost?
AName the files with a random hash to improve data distribution.
BStore all data in a single, very large file.
CUse S3 Transfer Acceleration to speed up Athena queries.
DPartition the data using a key-value format in the object prefix, such as `s3://bucket/year=2023/month=10/day=26/`.
Explanation
The best solution is to partition the data. Here's why: Partitioning is a critical optimization technique for data lakes. By structuring the S3 paths with key-value pairs, you enable Athena's 'partition pruning'. When you run a query with a `WHERE` clause on a partition key (e.g., `WHERE year=2023 AND month=10`), Athena will completely ignore all other folders. This drastically reduces the amount of data scanned, leading to faster queries and lower costs. Why the other options are incorrect: - A: A single large file would force Athena to scan the entire file for every query, which is highly inefficient. - C: S3 Transfer Acceleration is for speeding up file uploads to S3, not for improving query performance. - D: A random hash for a name prevents partition pruning and would make date-based queries impossible to optimize.
More AWS Practice Exams
AWS Certified CloudOps Engineer - Associate (SOA-C03)
SOA-C03 · 2141 questions
AWS Certified SysOps Administrator - Associate (SOA-C02)
SOA-C02 · 2141 questions
AWS Certified Security - Specialty (SCS-C03)
SCS-C03 · 2069 questions
AWS Certified Generative AI Developer - Professional (AIP-C01)
AIP-C01 · 1978 questions
AWS Certified Advanced Networking - Specialty (ANS-C01)
ANS-C01 · 1453 questions
AWS Certified Data Engineer - Associate (DEA-C01)
DEA-C01 · 1120 questions
$17.99
One-time access to this exam
Full access to all 582 questions
Or $15/mo for all 253 exams
Detailed explanations
Free preview stays available