The following table lists the exam objectives and their weightings.

OCI Data Science – Introduction & Configuration    

• Discuss OCI Data Science Overview & Concepts
• Discuss the capabilities of Accelerated Data Science(ADS) SDK
• Configure your tenancy for Data Science

Design and Set up OCI Data Science Workspace 

• Create and manage Projects and Notebook sessions
• Create and manage Conda environments
• Use OCI Vault to store credentials
• Configure and manage source code in Code Repositories (Git)

Implement end-to-end Machine Learning Lifecycle   

• Discuss ML Lifecycle Overview
• Use different data sources to fetch data.
• Explore and Prepare data
• Visualize and Profile data
• Create and train models using OCI and Open source Libraries
• Create and Use automated ML capability from Oracle AutoML
• Evaluate models
• Obtain Global & Local Model Explanations
• Manage models using Model Catalog
• Deploy & Invoke a Cataloged Model
• Discuss ADS and OCI Generative AI Integration
• Discuss LangChain Application deployment to Data Science.
• Discuss Operators (optional)
• Discuss AI Quick Actions

Apply MLOps Practices    

• Discuss OCI MLOps Architecture
• Create & Manage Jobs for custom tasks
• Scale with OCI Data Science
• Discuss Autoscaling Model deployment for Inference
• Monitor & Log using MLOps Practices
• Use Pipelines to automate machine learning workflow

Use related OCI Services       

• Create and Manage Spark Applications using Data Flow and OCI Data Science
• Describe OCI Open Data Service
• Create and Export a Dataset using OCI Data Labeling