AWS Sagemaker Practice Exam
AWS Sagemaker Practice Exam
About AWS Sagemaker Exam
The AWS SageMaker Exam is designed to assess a candidate’s proficiency in building, training, deploying, and managing machine learning (ML) models using Amazon SageMaker. SageMaker is a fully managed cloud service that allows developers and data scientists to prepare data, select algorithms, train models, tune hyperparameters, and scale ML workflows in production with minimal infrastructure management. The exam covers a wide range of topics including SageMaker Studio, built-in algorithms, model pipelines, notebook instances, model monitoring, and real-time inference endpoints. This certification is ideal for individuals working in AI/ML engineering, data science, or those seeking to scale AI solutions using AWS infrastructure.
Who should take the Exam?
This exam is ideal for:
- Machine learning engineers and data scientists using AWS tools
- AWS solution architects working with ML workflows
- AI professionals deploying models at scale in the cloud
- Software developers integrating SageMaker with applications
- DevOps engineers managing MLOps pipelines on AWS
Skills Required
- Understanding of machine learning lifecycle
- Familiarity with Python, Jupyter notebooks, and ML libraries
- Basic to intermediate experience with AWS services (S3, IAM, Lambda)
- Knowledge of data processing, training, and evaluation
Knowledge Gained
- End-to-end model development using SageMaker Studio
- Efficient training and tuning of ML models on AWS
- Using built-in, custom, and marketplace algorithms
- Monitoring model performance and automating retraining
Course Outline
The AWS Sagemaker Exam covers the following topics -
Domain 1 – Introduction to SageMaker and AWS ML Services
- Overview of SageMaker architecture and use cases
- Comparison with other AWS AI/ML services
- Setup of SageMaker Studio and notebook instances
Domain 2 – Data Preparation and Feature Engineering
- Data preprocessing using SageMaker Data Wrangler
- Using Amazon S3 and Athena for data input
- Feature store and schema management
Domain 3 – Model Training and Tuning
- Using built-in and custom algorithms
- Training jobs and managed Spot training
- Hyperparameter tuning with SageMaker Experiments
Domain 4 – Model Deployment and Inference
- Deploying models to real-time endpoints
- Batch transform jobs and multi-model endpoints
- Container-based deployments and scaling
Domain 5 – Model Monitoring and Drift Detection
- Setting up SageMaker Model Monitor
- Tracking prediction data and baseline statistics
- Creating alerts for performance and data drift
Domain 6 – MLOps and Automation with Pipelines
- Introduction to SageMaker Pipelines
- CI/CD integration using CodePipeline and CodeBuild
- Versioning, lineage tracking, and auditability
Domain 7 – Security, Governance, and Cost Optimization
- IAM roles and permissions in SageMaker
- Encryption, VPC configuration, and private endpoints
- Cost control using Spot Instances and monitoring tools
Domain 8 – Advanced Use Cases and Integrations
- Deploying deep learning models and NLP use cases
- Integrating with Lambda, API Gateway, and Step Functions
- Using SageMaker JumpStart and prebuilt solutions
