AWS Certified Machine Learning Engineer Associate PRACTICE

Contents

What You’ll Learn Requirements Description Who this course is for:

What
You’ll Learn

Comprehensive ML Lifecycle Skills
Data Preprocessing and Feature Engineering
Model Deployment and Maintenance
Performance Optimization and Cost Management

Requirements

ML and Deep Learning Experience
AWS Cloud Knowledge
Data Engineering Skills
Programming and Scripting

Description

“This practice exam consists of 6 sections, each containing 65 questions, covering all the topics included in the certification exam.”

The AWS Certified Machine Learning Engineer – Associate certification is designed for individuals focused on building and deploying machine learning (ML) models on AWS. This certification validates proficiency in machine learning concepts, such as data preparation, feature engineering, model training, and performance tuning, as well as deploying and maintaining ML solutions at scale. Here’s a breakdown of the main course elements and skill areas covered:

1. Data Engineering and Ingestion

Emphasis on data preprocessing and feature engineering
Hands-on work with AWS services such as AWS Glue, Amazon S3, and Athena for data cleaning, transformation, and ingestion tasks
Understanding data lakes and structured data ingestion, particularly for large datasets

2. Model Training and Tuning

Use of Amazon SageMaker for model training, tuning, and hyperparameter optimization
Proficiency with algorithms (e.g., XGBoost, linear learner, random forest) and evaluation metrics (e.g., precision, recall, accuracy) critical for training robust models
Knowledge of hyperparameter tuning, performance metrics, and handling model bias

3. Model Deployment and Operations

Deployment and management of models using SageMaker services such as Model Registry and Model Monitor
Skills in configuring models for inference, utilizing real-time and batch deployments, and understanding options for deploying scalable ML pipelines
Security considerations for deployment, including IAM configurations and VPC security practices

4. Automation and Machine Learning Pipelines

Integration of machine learning into automated workflows
Familiarity with services like AWS Step Functions and SageMaker Pipelines for streamlining ML processes from data ingestion to deployment

5. Responsible AI and Generative AI

Topics in responsible AI, including model explainability and bias detection using SageMaker Clarify
Introduction to generative AI, foundational models, and services like AWS Bedrock

Exam Format and Skills Required

The exam features questions that are hands-on and practical, with a focus on real-world configurations, case study analysis, and understanding of complex scenarios related to ML model lifecycle management on AWS. AWS recommends hands-on experience with SageMaker and related AWS ML tools, as well as proficiency in data science and ML concepts.

This associate-level certification serves as a strong foundation for professionals interested in machine learning, data engineering, and model deployment within the AWS ecosystem.

For additional insights and preparation resources, including study tips and recommended AWS Skill Builder content, check out AWS’s official training resources and third-party guides.