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AWS Sagemaker

AWS Sagemaker

Free Practice Test

FREE
  • No. of Questions100
  • AccessImmediate
  • Access DurationLife Long Access
  • Exam DeliveryOnline
  • Test ModesPractice
  • TypeExam Format

Practice Exam

$7.99
  • No. of Questions105
  • AccessImmediate
  • Access DurationLife Long Access
  • Exam DeliveryOnline
  • Test ModesPractice, Exam
  • Last UpdatedJuly 2025

Online Course

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  • Content TypeVideo
  • DeliveryOnline
  • AccessImmediate
  • Access DurationLife Long Access
  • No of videos-
  • No of hours-
Not Available

AWS Sagemaker


This exam validates your ability to design, develop, and manage complete machine learning solutions using Amazon SageMaker, a powerful managed service for scalable AI on AWS. You’ll gain practical experience in creating automated ML workflows, training models using custom and built-in algorithms, and deploying them to real-time and batch endpoints. The certification also tests your skills in integrating SageMaker with other AWS services for security, monitoring, automation, and governance. Whether you're a data scientist optimizing performance or a DevOps professional orchestrating ML pipelines, this exam equips you to lead cloud-native AI initiatives from experimentation to production-grade deployment.


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


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


    Course Outline

    • Introduction to SageMaker and AWS ML Services
    • Data Preparation and Feature Engineering
    • Model Training and Tuning
    • Model Deployment and Inference
    • Model Monitoring and Drift Detection
    • MLOps and Automation with Pipelines
    • Security, Governance, and Cost Optimization
    • Advanced Use Cases and Integrations

    AWS Sagemaker FAQs

    ML Engineer, AI Solutions Architect, Data Scientist, MLOps Engineer

    Professionals using AWS to build or scale machine learning models

    You will learn ML workflows, automation, inference deployment, and AWS integrations

    Designing scalable ML solutions, monitoring models, managing pipelines

    Yes, especially for deploying ML models in AWS environments or building end-to-end ML systems

    Useful if the candidate has prior exposure to ML fundamentals and cloud services

    Demonstrates your ability to productionize AI solutions and manage ML workflows on AWS

     

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