Oracle 1z0-1110-23 Exam Details & Actual Exam Questions
- Exam Code/Number: 1z0-1110-23
- Exam Name/Title: Oracle Cloud Infrastructure Data Science 2023 Professional
- Certification Provider: Oracle
- Corresponding Certification: Oracle Cloud
- Exam Questions: 80
- Updated On: Sep,07 2026
- Certification Level: Professional
Oracle Cloud Infrastructure Data Science 2023 Professional Exam Questions
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(44 Up Votes)Oracle 1z0-1110-23 Exam Overview:
| Certification Vendor: | Oracle |
|---|---|
| Exam Name: | Oracle Cloud Infrastructure Data Science 2023 Professional |
| Exam Number: | 1Z0-1110-23 |
| Available Languages: | English |
| Passing Score: | 65-70% |
| Exam Format: | Multiple Select, Multiple Choice |
| Exam Price: | $245 USD |
| Real Exam Qty: | 50-60 |
| Related Certifications: | Oracle Cloud Infrastructure Foundations Associate Oracle Cloud Infrastructure Machine Learning Specialist |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 90-120 |
| Recommended Training: | Oracle University OCI Data Science Training |
| Exam Registration: | Oracle Certification Registration |
| Sample Questions: | Oracle 1z0-1110-23 Sample Questions |
| Exam Way: | Online proctored exam via Oracle testing platform or authorized testing centers |
| Pre Condition: | Recommended understanding of cloud computing fundamentals and basic machine learning concepts. Prior OCI experience is beneficial but not strictly required. |
| Official Syllabus URL: | https://education.oracle.com/oracle-certification-exams |
Oracle 1z0-1110-23 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Model Development and Training | - Using notebooks and Python ML libraries - Training models using OCI Data Science |
| OCI Data Science Service Overview | - Core concepts of Oracle Cloud Infrastructure Data Science - Workspace, projects, and notebooks |
| Model Deployment and MLOps | - Model lifecycle management and CI/CD for ML - Model deployment endpoints |
| Machine Learning Algorithms and Evaluation | - Supervised and unsupervised learning methods - Model evaluation metrics and tuning |
| OCI Data and AI Services Integration | - Integration with OCI data services - Using AI services within OCI ecosystem |
| Data Preparation and Feature Engineering | - Feature engineering techniques - Data ingestion and transformation on OCI |
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