1Z0-1109-23 Retired: What Replaced It and What Carries Over

Historical 1Z0-1109-23 Scope

The Oracle Cloud Infrastructure 2023 Data Science Professional 1Z0-1109-23 exam covered data science methodology and OCI service implementation. This certification, provided by Oracle, is now retired. Candidates seeking professional validation in this field should instead pursue the successor exam, 1Z0-1109-26, which serves as the current path for demonstrating proficiency in OCI data science workflows, model development, and MLOps practices. This transition ensures that professionals remain aligned with the latest cloud-based machine learning standards and OCI service capabilities provided by Oracle for modern data science projects.

Who 1Z0-1109-23 Was For

This certification was intended for data scientists and machine learning engineers working with OCI. Candidates needed experience in building, training, and deploying models using OCI services. Professionals should now focus their efforts on the 1Z0-1109-26 exam to validate these skills.

Skills You Should Be Ready to Demonstrate

• Data science methodology
• OCI Data Science service management
• Machine learning model development
• Model training and evaluation
• Model deployment and MLOps
• Data preparation and feature engineering

How to Reuse Your Preparation

Focus your preparation on the 1Z0-1109-26 exam. Much of the technical knowledge regarding OCI Data Science services, such as notebook sessions, model catalog management, and pipeline orchestration, remains relevant. Prioritize learning the latest OCI service updates and best practices for MLOps. Use official Oracle documentation to master the end-to-end lifecycle of predictive models, as this knowledge transfers directly to the current certification requirements. Reviewing the updated service documentation will help you succeed on the new exam path.

Historical Domain Guide

Data Science Concepts and Methodology: Historical Scope

This domain covers the fundamental principles and methodologies required for data science projects within the OCI environment, focusing on the systematic approach to solving business problems using machine learning techniques and standard industry practices.

• Data science lifecycle stages
• Methodology application

OCI Data Science Service: Historical Scope

This domain addresses the OCI Data Science service architecture, including the use of collaborative workspaces, serverless infrastructure, and integration with other OCI services like Object Storage and Functions to manage complex data science workloads.

• Service architecture
• Resource management
• Integration patterns

Machine Learning Model Development: Historical Scope

This domain focuses on the development of machine learning models, utilizing Python-centric tools, libraries, and the Oracle Accelerated Data Science Library for building predictive models within the OCI Data Science service environment.

• Model development workflows
• ADS library usage

Model Training and Evaluation: Historical Scope

This domain covers the training and evaluation of models, including the use of Oracle AutoML for training and Oracle MLX for model explanation and interpretation to ensure high performance and transparency in models.

• AutoML implementation
• Model evaluation techniques

Model Deployment and MLOps: Historical Scope

This domain focuses on deploying models as web applications using HTTP API endpoints and managing MLOps tasks to ensure repeatable and scalable model production within the OCI cloud infrastructure for enterprise-grade machine learning solutions.

• Model deployment
• MLOps automation

Data Preparation and Feature Engineering: Historical Scope

This domain covers the tasks of data acquisition, profiling, preparation, and feature engineering required to ready datasets for machine learning model training, ensuring data quality and relevance for predictive modeling success.

• Data preparation
• Feature engineering