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Oracle 1Z0-1110-25 Exam Syllabus

Start Free 1Z0-1110-25 Exam Practice After Reviewing the Topics

Before starting your 1Z0-1110-25 exam preparation, it is recommended to review the complete Oracle Cloud Infrastructure 2025 Data Science Professional exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free 1Z0-1110-25 questions. We also provide premium 1Z0-1110-25 practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.

Oracle
Vendor
1Z0-1110-25
Exam Code
158
Total Questions
5
Total Exam Domains

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1Z0-1110-25 EXAM QUESTIONS

Oracle 1Z0-1110-25 Exam Objectives

Section 1: OCI Data Science - Introduction & Configuration
Weight:
10%
  • Discuss OCI Data Science Overview & Concepts
  • Discuss the capabilities of Accelerated Data Science(ADS) SDK
  • Configure your tenancy for Data Science
Section 2: Design and Set up OCI Data Science Workspace
Weight:
15%
  • 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)
Section 3: Implement end-to-end Machine Learning Lifecycle
Weight:
45%
  • 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
  • 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
Section 4: Apply MLOps Practices
Weight:
20%
  • 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
Section 5: Use related OCI Services
Weight:
10%
  • 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
Info
https://education.oracle.com/oracle-cloud-infrastructure-2024-data-science-professional/pexam_1Z0-1110-25