Oracle Machine Learning using Autonomous Database 2022 Specialist 1z0-1096-22 Certified Exam Dumps

1z0-1096-22 Exam Dumps

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Certification Provider: Oracle
Exam Code / Number: 1z0-1096-22
Exam Name: Oracle Machine Learning using Autonomous Database 2022 Specialist
Exam Questions: 75
Last Updated: Jul 01, 2026
Corresponding Certification: Oracle Autonomous Database

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To be eligible for the Oracle 1z0-1096-22 certification exam, candidates must have a minimum of two years of experience in using Oracle Database and at least six months of experience in using Oracle Machine Learning with Autonomous Database. They must also have completed the Oracle Machine Learning course or have equivalent knowledge and skills.

Reference: https://education.oracle.com/oracle-database-foundations-novice-level-exam/pexam_1Z0-1096-22

If you want to master Machine Learning and get certified on it, the Oracle 1z0-1096-22 Certification Exam is the ultimate solution for you. Oracle Machine Learning using Autonomous Database 2022 Specialist certification exam validates your knowledge of Machine Learning algorithms, AutoML, and data types that are required to deliver Machine Learning solutions using Oracle Autonomous Database. 1z0-1096-22 exam is for those who have a strong background in Data Science and who want to fulfill the demands of analyzing data with Machine Learning models using Oracle software products.

To prepare for the Oracle 1z0-1096-22 exam, candidates need to have a good grasp of machine learning concepts, such as supervised and unsupervised learning, regression, classification, clustering, and deep learning. Additionally, candidates must also be familiar with programming languages like Python and R, and have some experience in data preparation and visualization. They should also have a good understanding of database management concepts, particularly Oracle Autonomous Database.

Oracle 1z0-1096-22 Exam Syllabus Topics:

SectionWeightObjectives
Administration and Security10%- Access control and privileges
  • 1. User and role management for OML
  • 2. Security policies and data access permissions
  • 3. Monitoring and maintenance
OML Notebooks and Environment15%- Workspace and notebook management
  • 1. Creating projects, workspaces, and notebooks
  • 2. SQL, PL/SQL, Python, and R notebook execution
  • 3. Sharing, collaboration, and job scheduling
Oracle Machine Learning Fundamentals15%- Machine learning concepts and paradigms
  • 1. OML components and integration with Autonomous Database
  • 2. Supervised, unsupervised, and semi-supervised learning
  • 3. In-database machine learning architecture
Building and Evaluating Models25%- Model development and validation
  • 1. Model tuning and optimization
  • 2. Model performance metrics and evaluation
  • 3. Classification, regression, clustering, association rules
  • 4. OML4SQL, OML4Py, and OML4R usage
AutoML and Model Management15%- Automated machine learning
  • 1. Algorithm recommendation and model generation
  • 2. Model registration, versioning, and deployment
  • 3. AutoML experiments and feature selection
Data Preparation and Exploration20%- Data access and transformation
  • 1. Data profiling and visualization
  • 2. Feature engineering and normalization
  • 3. Handling missing values and outliers
  • 4. Using OML4SQL and OML Notebooks for data exploration


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