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Last Updated: Jul 26, 2026
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Oracle 1z0-1110-22 Exam consists of 80 multiple-choice questions, which should be completed within 105 minutes. 1z0-1110-22 exam questions are designed to test the candidate's knowledge and skills in various areas, such as machine learning algorithms, data management, data visualization, and Oracle Cloud Infrastructure Data Science.
The Oracle 1z0-1110-22 exam covers a wide range of topics, including data preparation, feature engineering, model training and evaluation, and deployment. Candidates will be tested on their ability to use various tools and technologies within the Oracle Cloud Infrastructure environment, including Oracle Cloud Infrastructure Data Science, Oracle Autonomous Database, and Oracle Machine Learning.
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To pass the Oracle 1z0-1110-22 exam, candidates must demonstrate a deep understanding of various data science concepts, including data exploration techniques, data visualization, data cleaning, and model evaluation. They must also be proficient in using Oracle Cloud Infrastructure services, such as Oracle Autonomous Database, Oracle Analytics Cloud, and Oracle Machine Learning. 1z0-1110-22 exam requires a thorough understanding of these services' features and how to use them to solve real-world data science problems.
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning Workflows on OCI | 25% | - Training, tuning, and evaluating models - Using JupyterLab and conda environments - Algorithm selection and model development - Creating and managing ML projects |
| Model Deployment and Operations | 20% | - Deploying models as endpoints - A/B testing and model versioning - Model monitoring and management - OCI Data Science Model Catalog |
| Integration with OCI Services | 15% | - Integration with OCI Functions for serverless inference - Using OCI Resource Manager and IaC - Security and IAM for Data Science resources - Logging and monitoring with OCI Logging and Monitoring |
| OCI Data Science Service Overview and Architecture | 15% | - Understanding OCI Data Science core concepts and features - OCI Data Science service architecture components - OCI Data Science vs. traditional data science environments |
| OCI Data Science API and SDK | 10% | - Using the OCI Python SDK for Data Science - Automating workflows with SDK - REST API interactions |
| Data Storage and Management for Data Science | 15% | - Data versioning and lifecycle management - Using OCI Object Storage for data assets - Integrating with OCI Data Flow for large-scale processing |
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