Oracle Cloud Infrastructure 2025 Data Science Professional Study Guide: Everything You Need to Know 2025
Your complete roadmap to passing the 1Z0-1110-25 certification exam. This comprehensive study guide covers all 4 exam domains with detailed explanations, study tips, and practice resources.
Quick Start
Essential steps to begin your preparation
Review Exam Objectives
View all domains →Take Assessment Quiz
Free practice test →Follow Study Plan
8-week roadmap →Full Practice Exams
Start practicing →Exam Domains & Objectives
Master these 4 domains to pass the 1Z0-1110-25 exam
OCI Data Science Service
Machine Learning Model Development
Model Deployment and Management
Data Engineering and MLOps
8-Week Study Plan
Follow this structured plan to prepare for your Oracle Cloud Infrastructure 2025 Data Science Professional exam
Foundation
Understand core concepts and exam objectives
Focus Areas:
- OCI Data Science Service
- Machine Learning Model Development
Deep Dive
Master advanced topics and practical applications
Focus Areas:
- Model Deployment and Management
- Data Engineering and MLOps
Practice & Review
Take practice exams and review weak areas
Focus Areas:
Final Prep
Full practice exams and last-minute review
Focus Areas:
- Full-length practice tests
- Review all domains
Curated Study Resources
AI-curated resources with real links to help you prepare for the Oracle Cloud Infrastructure 2025 Data Science Professional exam
Complete Study Guide for Oracle Cloud Infrastructure 2025 Data Science Professional
The Oracle Cloud Infrastructure 2025 Data Science Professional certification (1Z0-1110-25) validates your expertise in using OCI Data Science services, developing and deploying machine learning models, and implementing MLOps practices. This professional-level certification demonstrates your ability to architect and implement end-to-end data science solutions on Oracle Cloud Infrastructure.
Who Should Take This Exam
- Data Scientists working with Oracle Cloud Infrastructure
- Machine Learning Engineers implementing ML solutions on OCI
- Cloud Architects focusing on data science and AI workloads
- ML Engineers responsible for model deployment and operations
- Data Engineers working on MLOps pipelines
Prerequisites
- Strong understanding of machine learning concepts and algorithms
- Experience with Python programming and ML libraries (scikit-learn, TensorFlow, PyTorch)
- Familiarity with Oracle Cloud Infrastructure fundamentals
- Knowledge of data engineering and ETL processes
- Understanding of model deployment and MLOps practices
- Hands-on experience with Jupyter notebooks and data science workflows
Official Resources
Oracle Cloud Infrastructure Certification Homepage
Main certification portal with exam information and registration
View ResourceOracle Cloud Infrastructure Documentation
Complete OCI documentation covering all services
View ResourceOCI Data Science Service Documentation
Official documentation for OCI Data Science service, covering all features and capabilities
View ResourceOracle Learning Library - Data Science
Free tutorials and hands-on labs for Oracle technologies
View ResourceOCI Architecture Center - Data Science
Reference architectures and best practices for data science on OCI
View ResourceOracle MyLearn Platform
Oracle's official learning platform with courses, paths, and certifications
View ResourceRecommended Courses
Recommended Books
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
by Aurélien Géron
Comprehensive guide to machine learning with practical examples and implementation details
View on AmazonDesigning Machine Learning Systems
by Chip Huyen
End-to-end guide to building production ML systems with MLOps best practices
View on AmazonMachine Learning Engineering
by Andriy Burkov
Practical guide to deploying and maintaining ML models in production
View on AmazonFeature Engineering for Machine Learning
by Alice Zheng and Amanda Casari
Deep dive into feature engineering techniques and best practices
View on AmazonPractical MLOps
by Noah Gift and Alfredo Deza
Comprehensive guide to operationalizing machine learning systems
View on AmazonPractice & Hands-On Resources
Oracle Cloud Free Tier
Free OCI account with Always Free services including Data Science notebook sessions for hands-on practice
View ResourceOCI Data Science Examples and Tutorials
GitHub repository with example notebooks and tutorials for OCI Data Science
View ResourceOracle Learning Library - Hands-on Labs
Free interactive labs covering OCI Data Science features and workflows
View ResourceKaggle Datasets and Competitions
Practice ML model development with real-world datasets
View ResourceOracle MyLearn Practice Exams
Official practice tests available through Oracle's learning platform
View ResourceOCI SDK for Python Examples
Code examples demonstrating OCI service integration with Python
View ResourceCommunity & Forums
Oracle Cloud Infrastructure Community Forum
Official Oracle community for OCI discussions, questions, and knowledge sharing
Join CommunityReddit - r/oraclecloud
Community discussions about Oracle Cloud services, certifications, and best practices
Join CommunityReddit - r/MachineLearning
General ML discussions, research papers, and implementation strategies
Join CommunityOracle Developers Blog
Technical articles, tutorials, and best practices from Oracle developers
Join CommunityMedium - Oracle Developers Publication
Community articles and tutorials about Oracle technologies
Join CommunityStack Overflow - Oracle Cloud Tag
Technical Q&A for Oracle Cloud development and troubleshooting
Join CommunityLinkedIn Oracle Cloud Community
Professional networking and discussions about Oracle Cloud certifications
Join CommunityStudy Tips
Hands-on Practice is Critical
- Create an OCI Free Tier account immediately and use it throughout your study
- Build at least 5-10 complete ML projects from data ingestion to deployment
- Practice every feature of the Data Science service, not just reading about them
- Create notebook sessions with different compute shapes to understand configurations
- Deploy multiple models and test them via REST APIs
Master the ADS SDK
- The Accelerated Data Science SDK is central to OCI Data Science - know it thoroughly
- Practice all ADS functions: model saving, loading, evaluation, and explanation
- Understand how ADS integrates with the Model Catalog
- Learn ADS methods for data loading from Object Storage and databases
- Familiarize yourself with ADS AutoML capabilities and configurations
Understand Service Integration
- Learn how Data Science integrates with Object Storage, ADW, and other OCI services
- Practice creating end-to-end pipelines using multiple services
- Understand IAM policies required for Data Science service access
- Know how to configure VCN and networking for Data Science projects
- Practice integrating with OCI Functions and API Gateway for model serving
Focus on MLOps and Production Patterns
- Understand the complete model lifecycle from development to retirement
- Practice creating automated training and deployment pipelines
- Learn model monitoring, logging, and alerting strategies
- Know different deployment patterns (batch vs real-time, A/B testing)
- Understand when to retrain models and how to automate it
Study Model Development Thoroughly
- Don't just memorize algorithms - understand when to use each one
- Practice feature engineering techniques on real datasets
- Know various evaluation metrics and which to use for different problems
- Understand hyperparameter tuning strategies and their trade-offs
- Learn model explainability methods and how to implement them in OCI
Documentation is Your Friend
- Bookmark all relevant OCI documentation pages for quick reference
- Read through the entire Data Science service documentation at least twice
- Review API references for common operations
- Study architecture diagrams and reference architectures
- Keep release notes and new features documentation handy
Time Management During Study
- Allocate 30% of time to OCI Data Science service specifics
- Spend 25% each on ML development and deployment topics
- Dedicate 20% to data engineering and MLOps
- Reserve last 2 weeks for review and practice exams
- Create summary notes after each study session for quick review
Practice Exam Strategy
- Take at least 3 full-length practice exams under timed conditions
- Review all incorrect answers thoroughly - understand why you got them wrong
- Identify patterns in questions - certain topics may appear frequently
- Time yourself: 55 questions in 90 minutes = less than 2 minutes per question
- Practice eliminating obviously wrong answers first
Exam Day Tips
- 1Arrive 15 minutes early if taking exam at a test center, or prepare your space 30 minutes before for online proctoring
- 2Read each question carefully - some may have multiple correct answers with one being 'most correct'
- 3Watch for keywords like 'best practice', 'most efficient', 'least expensive', 'most secure'
- 4If unsure, eliminate obviously wrong answers first, then make an educated guess
- 5Flag difficult questions and return to them after completing easier ones
- 6You need 38 correct answers out of 55 to pass (68%) - don't panic if some questions seem very difficult
- 7OCI-specific questions often focus on service limits, pricing models, and best practices
- 8For scenario questions, identify the core requirement before selecting an answer
- 9Don't spend more than 2 minutes on any single question - move on and come back if needed
- 10Review all flagged questions if time permits before submitting the exam
- 11Remember that hands-on experience is the best preparation - real-world scenarios help immensely
- 12Stay calm and confident - you've prepared thoroughly with hands-on practice
Study guide generated on January 7, 2026
Pro Study Tips
Expert advice to maximize your study effectiveness
Active Learning Strategies
- Hands-on practice: Apply concepts in real scenarios
- Teach others: Explain concepts to reinforce learning
- Take notes: Write summaries in your own words
Exam Day Preparation
- Get enough sleep: Rest well the night before
- Review key points: Go through your notes and cheat sheets
- Time management: Practice pacing with timed exams
Continue Your Preparation
More resources to help you succeed
Complete Oracle Cloud Infrastructure 2025 Data Science Professional Study Guide
This comprehensive study guide will help you prepare for the 1Z0-1110-25 certification exam offered by Oracle. Whether you are a beginner or experienced professional, this guide covers everything you need to know to pass on your first attempt.
What You Will Learn
Our study guide covers all 4 exam domains in detail:
- OCI Data Science Service (30%)
- Machine Learning Model Development (25%)
- Model Deployment and Management (25%)
- Data Engineering and MLOps (20%)
Recommended Timeline
Most candidates need 6-8 weeks of dedicated study to pass the Oracle Cloud Infrastructure 2025 Data Science Professional exam. We recommend studying 1-2 hours daily and taking practice exams weekly to track your progress.
Next Step: Start with our free practice test to assess your current knowledge level.