Oracle Cloud Infrastructure Data Science Professional Quick Facts (2025)

Oracle Cloud Infrastructure Data Science Professional Quick Facts

The Oracle Cloud Infrastructure (OCI) Data Science Professional certification empowers you to master the complete lifecycle of machine learning solutions on OCI. This overview provides clear insights, structure, and focus areas to help you prepare confidently and advance your expertise in enterprise-scale, cloud-based data science.

What does the Oracle Cloud Infrastructure Data Science Professional certification demonstrate?

This certification validates your ability to design, build, deploy, and manage machine learning solutions using OCI’s Data Science service and integrated ecosystem. It highlights your practical knowledge in leveraging the Accelerated Data Science (ADS) SDK, configuring secure and scalable workspaces, applying MLOps practices, and integrating with related OCI services such as Data Flow, Data Labeling, and Open Data. Earning this certification reflects your capability to lead end-to-end ML projects from data ingestion to deployment on Oracle Cloud.

Exam Domains Covered (Click to expand breakdown)

Exam Domain Breakdown

Domain 1: OCI Data Science - Introduction & Configuration (10% of the exam)

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.

Summary:
This section ensures you understand how Oracle Cloud Infrastructure supports modern data science workflows. You will review the key features and architecture of OCI Data Science, learn how it simplifies the machine learning process, and understand how it integrates with OCI’s ecosystem to support scalable experimentation and model deployment.

The section also explores the Accelerated Data Science (ADS) SDK, emphasizing its capabilities for data exploration, feature engineering, model training, and explainability. You’ll configure tenancy settings to enable data science services securely and efficiently, gaining practical insight into preparing an OCI environment ready for analytical innovation.

Domain 2: Design and set up Data Science Workspace (15% of the exam)

Design and set up 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).

Summary:
In this domain, you’ll become proficient at setting up productive environments where data scientists can collaborate and innovate. You will create and organize projects and notebook sessions, manage Conda environments to support diverse dependencies, and ensure smooth experimentation using reproducible configurations.

This section also teaches the importance of secure operations and version control. You’ll integrate credential management using OCI Vault, establish best practices for Git-based code repositories, and understand how structured workspace organization promotes team efficiency across the lifespan of machine learning initiatives.

Domain 3: Implement end-to-end Machine Learning Lifecycle (45% of the exam)

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.

Summary:
This domain is the core of the certification and covers the complete machine learning lifecycle from data ingestion through deployment. You will gain practical experience applying OCI Data Science and the ADS SDK to preprocess data, engineer features, visualize distributions, and train models with both OCI-integrated and open-source tools. Automated ML capabilities and evaluation techniques are central here, including model explainability through global and local interpretability methods.

Additionally, you will master managing trained models with the OCI Model Catalog, deploying them as endpoints, and monitoring predictive services. You’ll also explore modern integrations such as Generative AI, LangChain application deployment, and AI Quick Actions. The content emphasizes building end-to-end solutions that combine performance, governance, and automation for scalable data science delivery.

Domain 4: Apply MLOps Practices (20% of the exam)

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.

Summary:
This domain focuses on bringing software engineering discipline into the machine learning lifecycle. It highlights OCI MLOps architecture, showing how to automate and manage ML pipelines for consistent performance across environments. You will create and schedule custom jobs, apply logging and monitoring for reliability, and learn scaling strategies to accommodate dynamic workloads.

Emphasis is placed on deploying resilient, monitored systems that maintain integrity through automation. You will integrate OCI’s orchestration tools and best practices to streamline training, deployment, and inference, ensuring that models remain accurate, traceable, and cost-efficient in production.

Domain 5: Use related OCI Services (10% of the exam)

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.

Summary:
The final domain expands your understanding of connected Oracle Cloud services that complement OCI Data Science. You’ll learn to build and manage distributed processing jobs through Data Flow, handle large-scale data transformations, and explore open datasets using the OCI Open Data Service.

You’ll also master the use of OCI Data Labeling to create and curate datasets for supervised learning models. This integration of related services enhances every stage of the ML workflow—from data preparation to inference—allowing you to deliver advanced analytics and AI-driven intelligence solutions that operate seamlessly across OCI’s unified platform.

Who Should Earn the Oracle Cloud Infrastructure Data Science Professional Certification?

The Oracle Cloud Infrastructure Data Science Professional Certification is designed for professionals who want to showcase their expertise in building, training, deploying, and managing machine learning models using Oracle Cloud. It’s perfect for:

• Data scientists working in enterprise environments
• AI and ML practitioners who want to leverage OCI’s managed machine learning platform
• Cloud engineers and architects expanding into data science
• Technical leaders responsible for implementing end-to-end AI solutions
• Professionals seeking advanced OCI credentials beyond foundational-level certifications

This certification represents mastery in data science workflows on Oracle Cloud, combining open-source capabilities with the powerful OCI ecosystem.

What Career Opportunities Can This Certification Unlock?

This professional-level certification positions you for highly rewarding, data-driven roles. With it, you can explore careers such as:

• Data Scientist
• Machine Learning Engineer
• MLOps Engineer
• AI Solutions Architect
• Cloud Data Engineer

Organizations increasingly rely on certified experts to develop and operationalize AI solutions. Holding this credential signals that you have both the theoretical understanding and the practical OCI experience to drive data science innovation in real-world projects.

Which Exam Version Should You Take?

Candidates should register for the most up-to-date Oracle Cloud Infrastructure exam: Exam 1Z0-1110-25. This version has been validated against Oracle Cloud Infrastructure 2025, ensuring that your knowledge aligns with the latest OCI capabilities across machine learning, MLOps, and generative AI integrations.

How Many Questions Are on the OCI 1Z0-1110-25 Exam?

You’ll encounter 50 multiple-choice questions in the exam. Expect a mix of concepts, scenario-based items, and practical questions assessing your ability to apply data science principles on OCI. Some questions may include multi-select options, so always choose all correct responses where applicable.

How Long Is the Exam?

The exam gives you 90 minutes to complete all questions. Managing your time effectively is key; allocate a few minutes per question to ensure you can review your answers before submission. The duration provides ample time if you’re familiar with OCI’s data science environment and workflows.

What’s the Passing Score for the Oracle Cloud Infrastructure Data Science Professional Exam?

To pass the OCI Data Science Professional (1Z0-1110-25) exam, you’ll need a minimum of 68%. This score demonstrates your proficiency in applying Oracle’s machine learning solutions to end-to-end workflows. Achieving this mark confirms that you can confidently operate within OCI’s data science ecosystem.

How Much Does the Oracle Cloud Infrastructure Data Science Professional Exam Cost?

The certification exam costs $245 USD. Depending on your region, applicable taxes may vary. Oracle often provides learning subscriptions and discounts to organizations, students, and returning certification holders, making it a worthwhile investment in your cloud career.

What Language Is the Exam Offered In?

Currently, the Oracle Cloud Infrastructure Data Science Professional exam is available in English. As Oracle expands its global training reach, additional language options may be introduced in the future, so always check the official Oracle site for updates before scheduling.

What Topics Are Covered in the Oracle Cloud Infrastructure Data Science Certification?

The exam blueprint is divided into five major content areas:

1. OCI Data Science - Introduction & Configuration (10%)

   • Overview of OCI Data Science concepts and ADS SDK capabilities
   • Configuring tenancy for Data Science

2. Design and Set Up Data Science Workspace (15%)

   • Managing notebook sessions, projects, and environments
   • Using OCI Vault and Git repositories

3. Implement End-to-End Machine Learning Lifecycle (45%)

   • Data ingestion, preparation, training, and evaluation
   • Model management with Catalog, AutoML, Explanations, Generative AI

4. Apply MLOps Practices (20%)

   • Setting up jobs, pipelines, and monitoring deployments

5. Use Related OCI Services (10%)

   • Integration with Data Flow, Open Data Service, and Data Labeling

These domains measure your ability to execute all stages of the machine learning journey using OCI’s data science tools.

Are There Any Prerequisites?

While there are no strict prerequisites, Oracle recommends that candidates possess:

• Hands-on experience with OCI Data Science and related services
• Understanding of machine learning principles and lifecycle practices
• Familiarity with open-source ML frameworks and MLOps concepts

Practical knowledge gained through labs and field work will give you a significant edge when taking the exam.

What Skills Should You Focus on Before Taking the Exam?

To maximize your success, build practical expertise in the following areas:

• Configuring an OCI tenancy for data science workloads
• Creating and managing notebook sessions and Conda environments
• Training, evaluating, and deploying ML models
• Implementing AutoML, ADS SDK, and OCI Generative AI APIs
• Building MLOps pipelines with automation and monitoring tools
• Using OCI Vault, Data Labeling, and OCI Data Flow

The best way to master these skills is through hands-on practice with OCI’s Data Science platform.

How Is the OCI Data Science Professional Exam Structured?

The exam follows a multiple-choice format, including both single-answer and multiple-answer questions. Each question tests your ability to translate real-world ML and MLOps challenges into OCI-based solutions. Be prepared for scenario-based items that require applied understanding of data workflows.

What Is the Certification Validity Period?

Oracle certifications are subject to the Cloud Recertification Policy, which ensures your credential reflects the latest Oracle Cloud versions. This means your certification remains valid as long as the associated product version (OCI Data Science 2025) is active. You can stay certified by completing future recertifications through Oracle University.

How Can You Prepare for the Oracle Cloud Infrastructure Data Science Professional Exam?

Preparation combines both theoretical and practical learning. Oracle recommends:

1. Official Training – The Become an OCI Data Science Professional course provides structured instruction and labs.
2. Hands-On Practice – Get real-world OCI experience through interactive labs and projects.
3. Study Guides and Documentation – Review OCI documentation and ADS SDK guides.
4. Practice Exams – Reinforce your knowledge with realistic practice questions.

Boost your readiness by using top-rated Oracle Cloud Infrastructure Data Science Professional practice exams that mirror the actual testing environment and provide detailed feedback to help you improve confidently.

How Demanding Is the Oracle Cloud Infrastructure Data Science Professional Exam?

This certification is designed for professionals comfortable with both cloud and machine learning concepts. Candidates should understand data pipelines, feature engineering, model lifecycle management, and service integrations. With preparation and real-world exposure, most learners find the exam a fulfilling milestone in their cloud journey.

What’s Included in the ML Lifecycle Portion of the Exam?

The Implement End-to-End Machine Learning Lifecycle domain covers nearly half of the exam weight. You’ll demonstrate your ability to:

• Work with multiple data sources
• Clean and preprocess data
• Build, train, and evaluate models
• Use AutoML and explainability tools
• Deploy and manage models in production

This section highlights your competence in managing the entire data science process on OCI.

What Are MLOps Practices Tested in the Exam?

Expect to prove your understanding of OCI’s comprehensive MLOps framework, including:

• Job creation and scheduling
• Automating workflows with pipelines
• Autoscaling model deployments
• Monitoring model performance and logging metrics

The goal is to ensure candidates can manage ML operations that are scalable, reliable, and maintainable across cloud workloads.

What Are Related OCI Services You Should Know?

Beyond core data science, you should understand how OCI connects various services for data projects:

• OCI Data Flow for running Spark applications
• OCI Open Data Service for accessing publicly available datasets
• OCI Data Labeling for creating and exporting datasets

Integrating these services enhances your ability to create complete, production-ready machine learning systems.

How Long Should You Prepare for the OCI Data Science Professional Certification?

The ideal preparation timeline varies depending on prior experience. Candidates with strong machine learning backgrounds typically spend 4–8 weeks preparing, while those new to OCI may need additional time to complete training courses and hands-on labs. Consistency is more important than speed—commit to steady daily learning.

How Do I Register for the Oracle Cloud Infrastructure Data Science Professional Exam?

Registration is simple through Oracle University’s platform. You can purchase your exam and redeem it within six months. When ready, you can schedule your test either online or at a Pearson VUE testing center near you.

Where Can I Learn More and Schedule My Exam?

For official details, registration, policies, and schedule options, visit the Oracle Cloud Infrastructure Data Science Professional Certification page. This page also includes links to recommended training and learning subscriptions.

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The Oracle Cloud Infrastructure Data Science Professional Certification is a gateway to mastering AI and machine learning on Oracle Cloud. With structured preparation, real-world practice, and focus on practical application, you’ll soon join the ranks of Oracle-certified data professionals shaping the future of intelligent cloud innovation.