AWS · MLA-C01
AWS Certified Machine Learning Engineer - Associate (MLA-C01) Practice Tests
Validates ability to build, operationalize, deploy, and maintain machine learning solutions and pipelines using AWS Cloud services.
Exam Details
Practice Questions
582
≈ 8 practice exams
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. The full bank has 582 questions, enough for 8 full-length practice exams.
Preview — answers shown1. An engineer has built a custom inference container and deployed it to SageMaker. When they invoke the endpoint, it fails. The CloudWatch logs for the endpoint show the error: `OSError: [Errno 98] Address already in use`. What is the most likely cause of this error inside the container?
AThe web server process (e.g., Gunicorn, Flask) is configured to listen on a port other than 8080.
BThe web server process is trying to start multiple times or another process is already using port 8080 inside the container.
CThe Dockerfile did not use the `EXPOSE` instruction.
DThe IAM role for the endpoint is missing permissions.
Explanation
'Address already in use' means another process has already bound to the port. Here's why: This error is a standard Linux/networking error indicating that a program tried to bind to a network port (in SageMaker's case, port 8080) that was already occupied by another running process. This often happens in custom containers if the `ENTRYPOINT` or `CMD` script is misconfigured, causing the web server to be launched multiple times. It could also happen if a different, unexpected process starts up and grabs the port before the main web server does. Why the other options are incorrect: - A: The `EXPOSE` instruction is purely informational and does not actually open the port; it's the server process that does. - B: Using the wrong port would likely cause a connection timeout, as SageMaker would be trying to connect to 8080 and find nothing. - D: An IAM issue would result in an `AccessDenied` error, not a low-level OS error like this.
2. What is the purpose of the `EndpointConfig` resource in the SageMaker deployment process?
AIt is a resource that points to the model artifact in S3 and the inference container.
BIt is a configuration file that contains the hyperparameters used for training.
CIt is an immutable configuration template that defines the instance type, model(s), and other properties for an endpoint. It allows for blue/green deployments.
DIt is the live, running endpoint that serves predictions.
Explanation
The EndpointConfig is an immutable configuration template for an endpoint. Here's why: The Endpoint Configuration is a critical, separate resource. It defines all the properties of a deployment: which model(s) to use, what kind of instances to run them on, and how to weight traffic between them. Because it is an immutable resource, it enables safe deployments. To update an endpoint, you create a *new* EndpointConfig and then point the existing endpoint to it. This allows for easy rollbacks, as you can simply point the endpoint back to the old, known-good EndpointConfig. Why the other options are incorrect: - A: This describes the Endpoint resource itself. - B: This describes the Model resource. - D: This is not related to deployment.
3. A model training job is taking too long due to the slow I/O performance of reading data from Amazon S3. To accelerate this, the team decides to use a high-performance file system that is optimized for fast, parallel access to training data. Which AWS storage service is the best choice for this high-throughput training scenario?
AAmazon EFS
BAmazon S3 Glacier
CAmazon EBS with gp3 volumes
DAmazon FSx for Lustre
Explanation
Amazon FSx for Lustre is the best choice for maximizing I/O performance for ML training. It is a fully managed, high-performance file system specifically designed for compute-intensive workloads. It provides extremely high throughput and low-latency access, making it ideal for I/O-bound training jobs that need to process large datasets quickly. It can be linked to an S3 bucket to act as a high-speed cache. Why the others are wrong: - Amazon EFS is a general-purpose file system but doesn't offer the same level of extreme performance as FSx for Lustre. - EBS volumes are attached to a single instance and don't solve the problem for distributed training. - S3 Glacier is an archival service and is completely unsuitable for this task.
4. A company is deploying a complex, real-time inference application that consists of three separate microservices in containers: a feature preprocessor, the model server, and a results post-processor. They need fine-grained control over the networking, scaling, and lifecycle of these containers as a single cohesive application. Which AWS service is most suitable for this use case?
AA standard SageMaker real-time endpoint.
BAn AWS Batch job.
CA container orchestration service like Amazon ECS or Amazon EKS.
DAn AWS Lambda function with a container image.
Explanation
A container orchestrator like ECS or EKS is the most suitable service. Here's why: While SageMaker endpoints are excellent for single-model deployment, they abstract away orchestration. When you have a multi-container microservices application, you need an orchestrator to manage the complexity. Amazon ECS (Elastic Container Service) and EKS (Elastic Kubernetes Service) are designed for this. They allow you to define how your containers should run together, manage their networking, handle service discovery, and implement independent scaling policies for each microservice, providing the required control. Why the other options are incorrect: - A: A SageMaker endpoint is not designed for multi-service applications (though an Inference Pipeline can handle a simple linear chain). - B: Lambda is for serverless functions, not for orchestrating complex, long-running applications. - D: AWS Batch is for offline batch computing, not for serving real-time applications.
5. To deploy a SageMaker model using a single AWS CloudFormation template, which three resources must be defined in the correct order of dependency?
A1. `AWS::SageMaker::Endpoint`, 2. `AWS::SageMaker::Model`, 3. `AWS::SageMaker::EndpointConfig`
B1. `AWS::SageMaker::Model`, 2. `AWS::SageMaker::EndpointConfig`, 3. `AWS::SageMaker::Endpoint`
C1. `AWS::SageMaker::Model`, 2. `AWS::SageMaker::Endpoint`, 3. `AWS::SageMaker::EndpointConfig`
D1. `AWS::SageMaker::EndpointConfig`, 2. `AWS::SageMaker::Endpoint`, 3. `AWS::SageMaker::Model`
Explanation
The correct sequence is Model -> EndpointConfig -> Endpoint. Here's why: There is a strict dependency order. 1. **Model:** You must first define the `Model` resource, which points to your model artifact in S3 and inference container. 2. **EndpointConfig:** You then define the `EndpointConfig`, which references the `Model` and specifies the instance type and other deployment settings. 3. **Endpoint:** Finally, you define the `Endpoint`, which references the `EndpointConfig` to actually provision the live infrastructure. Why the other options are incorrect: - All other options violate this dependency chain. For example, you cannot create an Endpoint without first having an EndpointConfig.
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