Oracle 1z0-1110-22, also known as the Oracle Cloud Infrastructure Data Science 2022 Professional Exam, is a certification exam designed for data scientists who are interested in validating their skills and expertise in using Oracle Cloud Infrastructure for data science projects. 1z0-1110-22 exam is aimed at professionals who have experience in building and deploying machine learning models, data analysis, and data visualization using Oracle Cloud Infrastructure.
Oracle Cloud Infrastructure Data Science 2022 Professional certification exam is designed to test the skills and knowledge of professionals working in the field of data science. 1z0-1110-22 exam covers a range of topics related to data preparation, model development, and deployment, as well as advanced topics such as model performance optimization and security. Professionals who earn the certification gain increased credibility and recognition in the field of data science, as well as better job opportunities and higher salaries. To prepare for the exam, professionals should have a strong background in data science and machine learning and should take advantage of a range of study materials.
Reference: https://education.oracle.com/oracle-cloud-infrastructure-data-science-2022-professional/pexam_1Z0-1110-22
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To prepare for the Oracle 1z0-1110-22 exam, candidates should have a strong understanding of machine learning concepts, algorithms, and tools. They should also have experience using Python programming language and working with data in various formats. Oracle offers training courses and certification preparation resources to help candidates prepare for the exam. Upon passing the exam, candidates will earn the Oracle Cloud Infrastructure Data Science 2022 Professional certification, which demonstrates their mastery of machine learning techniques and their ability to use Oracle Cloud Infrastructure Data Science to build and deploy machine learning models.
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Oracle 1z0-1110-22 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Machine Learning Workflows on OCI | 25% | - Using JupyterLab and conda environments
- Algorithm selection and model development
- Training, tuning, and evaluating models
- Creating and managing ML projects
|
| OCI Data Science API and SDK | 10% | - REST API interactions
- Automating workflows with SDK
- Using the OCI Python SDK for Data Science
|
| OCI Data Science Service Overview and Architecture | 15% | - OCI Data Science vs. traditional data science environments
- OCI Data Science service architecture components
- Understanding OCI Data Science core concepts and features
|
| Integration with OCI Services | 15% | - Logging and monitoring with OCI Logging and Monitoring
- Using OCI Resource Manager and IaC
- Security and IAM for Data Science resources
- Integration with OCI Functions for serverless inference
|
| Data Storage and Management for Data Science | 15% | - Integrating with OCI Data Flow for large-scale processing
- Data versioning and lifecycle management
- Using OCI Object Storage for data assets
|
| Model Deployment and Operations | 20% | - A/B testing and model versioning
- Model monitoring and management
- OCI Data Science Model Catalog
- Deploying models as endpoints
|