Course and practice questions
Machine Learning Engineer course
Machine Learning Engineer – Associate (MLA-C02): 4 modules, 12 lessons and 4 quizzes. This exam is multiple choice, so the practice is the course and original questions. The lab runtime is kubeadm clusters only, and this page does not include an AWS account.
What's included
A course and practice questions
4 modules, 12 lessons and 4 quizzes (32 original questions). About 2 h 16 min of reading. The lab runtime is kubeadm clusters only, so this page does not include an AWS account.
4 modules
12 lessons mapped to the domains in the public exam guide.
4 quizzes
32 original practice questions, with an explanation for every answer. They are not exam items.
170-minute exam
The length published for this exam. This page does not offer a timed mock.
Exam domains
Domains from the public guide
The domain weights below sum to 100. The official page is the source of truth: Machine Learning Engineer – Associate exam page.
Data Preparation for ML and AI
28%of the exam
3 competencies
- Collect and store data
- Perform data transformation, feature engineering, and pre-processing
- Validate data quality and manage bias
ML Model and Foundation Model (FM) Development
24%of the exam
3 competencies
- Choose appropriate modeling approaches for ML and AI solutions
- Train, fine-tune, and customize models for ML and AI solutions
- Analyze and evaluate the performance of ML and AI systems
Deployment and Orchestration of ML and AI Workflows
24%of the exam
3 competencies
- Manage deployment infrastructure for ML and AI model types
- Provision and configure resources for ML and AI workloads based on existing architecture and requirements
- Implement automated orchestration and CI/CD pipelines for MLOps and AI workloads
Operating, Monitoring, and Securing ML and AI Solutions
24%of the exam
3 competencies
- Monitor ML and AI model inference and performance
- Optimize and manage ML and AI infrastructure costs and performance
- Secure ML and AI workloads and model endpoints
Curriculum
4 modules, 12 lessons, 4 quizzes
Lesson titles from the course. The questions stay in the quizzes, and the answers are not published here.
01Data preparation for ML and AI
How data is collected and stored, how it is turned into features or chunks, and how quality and bias are checked before a model or a foundation model sees it. Domain: Data Preparation for ML and AI.
- lessonCollect and store data11 min
- lessonTransform, features, and chunks12 min
- lessonQuality, bias, and what you refuse to train on11 min
02Model and foundation model development
Choose a modeling approach, customize it, and judge whether the result is good enough to ship. Domain: ML Model and Foundation Model (FM) Development.
- lessonChoose a modeling approach11 min
- lessonTrain, fine-tune, and customize11 min
- lessonEvaluate models and generative systems12 min
03Deployment and orchestration
Pick an inference pattern, provision the resources it needs, and put training and release on a pipeline. Domain: Deployment and Orchestration of ML and AI Workflows.
- lessonDeployment infrastructure12 min
- lessonProvision, scale, and keep agent state11 min
- lessonPipelines for models, prompts, and indexes11 min
04Operate, monitor, and secure
Watch live inference, keep the bill tied to the workload, and lock down the path to the model. Domain: Operating, Monitoring, and Securing ML and AI Solutions.
- lessonMonitor inference and agent behavior11 min
- lessonCost and performance of ML and AI systems11 min
- lessonSecure workloads, pipelines, and model endpoints12 min
Read the Machine Learning Engineer – Associate course
Sign up free to read the 12 lessons and take the 4 quizzes. The lab runtime is kubeadm clusters only, so this course does not include an AWS account.