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Oracle 1z0-1110-22 exam is designed for professionals who want to validate their skills and knowledge in Oracle Cloud Infrastructure Data Science. 1z0-1110-22 exam is focused on testing the candidate's ability to create and manage machine learning models, deploy them on the cloud, and use them to analyze data. Passing 1z0-1110-22 exam demonstrates that the candidate has the knowledge and skills required to work as a professional data scientist in the Oracle Cloud Infrastructure environment.
Reference: https://education.oracle.com/oracle-cloud-infrastructure-data-science-2022-professional/pexam_1Z0-1110-22
Oracle 1z0-1110-22 exam is intended for data scientists, machine learning engineers, and other professionals who are responsible for developing and deploying data science solutions on Oracle Cloud Infrastructure. Candidates should have a strong foundation in machine learning algorithms, data analysis, and programming. They should also be familiar with cloud computing concepts and have experience working with big data technologies.
Achieving the Oracle 1z0-1110-22 certification demonstrates that a candidate has the skills and knowledge required to work with Oracle Cloud Infrastructure for data science. Oracle Cloud Infrastructure Data Science 2022 Professional certification is particularly valuable for those working in roles such as data scientists, data analysts, and machine learning engineers who are looking to enhance their skills and advance their careers. The Oracle 1z0-1110-22 exam is a testament to a candidate's expertise and commitment to using the latest technologies and tools to develop innovative solutions that deliver business value.
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Oracle 1z0-1110-22 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Model Deployment and Operations | 20% | - Model monitoring and management
- OCI Data Science Model Catalog
- A/B testing and model versioning
- Deploying models as endpoints
|
| Data Storage and Management for Data Science | 15% | - Using OCI Object Storage for data assets
- Integrating with OCI Data Flow for large-scale processing
- Data versioning and lifecycle management
|
| Integration with OCI Services | 15% | - Security and IAM for Data Science resources
- Logging and monitoring with OCI Logging and Monitoring
- Using OCI Resource Manager and IaC
- Integration with OCI Functions for serverless inference
|
| Machine Learning Workflows on OCI | 25% | - Algorithm selection and model development
- Using JupyterLab and conda environments
- Training, tuning, and evaluating models
- Creating and managing ML projects
|
| OCI Data Science Service Overview and Architecture | 15% | - OCI Data Science service architecture components
- OCI Data Science vs. traditional data science environments
- Understanding OCI Data Science core concepts and features
|
| OCI Data Science API and SDK | 10% | - Automating workflows with SDK
- Using the OCI Python SDK for Data Science
- REST API interactions
|