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New OCI certification - Data Science 2022 Professional (1z0-1110-22)

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We have a new cloud exam available: Oracle Cloud Infrastructure Data Science 2022 Professional (1z0-1110-22). The 2021 ML certification is retiring in August and this is probably the replacement.

Update: the official article here.


These are the exam topics:

  • Configure your tenancy for OCI Data Science
    • Discuss OCI Data Science Overview & Concepts
    • Discuss Accelerated Data Science (ADS) SDK Capabilities
    • 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
    • Store credentials via OCI Vault
    • Configure and manage source code in Code Repositories (Git)
  • Implement end-to-end Machine Learning Lifecycle
    • Discuss ML Lifecycle Overview
    • Access data from different sources
    • 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
  • Apply MLOps Practices
    • Discuss general MLOps Architecture in OCI
    • Create & Manage Jobs for custom tasks
    • Scale with OCI Data Science
    • Monitor & Log using MLOps Practices
  • Use related OCI Services
    • Create and Manage Spark Applications using Data Flow and OCI Data Science
    • Explain core OCI Open Data Service concepts
    • Create and Export a Dataset using OCI Data Labeling
    • Use OCI AI Services for ML Solutions


Learning material

The official learning material Become an OCI Data Science Professional is available under OCI Learning Subscription and it is 13 hours long.

This learning path covers Oracle Cloud Infrastructure (OCI) Data Science, a service that supports the full machine learning life cycle, enabling data scientists to rapidly build, train, deploy, and manage machine learning models using Python and open source libraries. Users work in a familiar JupyterLab notebook interface, track and store their models in a model catalog, and operationalize them on managed infrastructure using best MLOps practices.

You will learn to:

  • Identify OCI services to implement an ML solution for a business use case.
  • Incorporate ML and cloud best practices.
  • Use Oracle Cloud Infrastructure Data Science to ingest, design, build, train, optimize, deploy, integrate, and maintain ML models.
  • Apply OCI Data & AI services to create ML solutions.

Related courses

Under the OLS there are few more related and interesting courses:

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