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DB ML Autonomous
The certification validates expertise in Oracle Machine Learning on Autonomous Database, covering fundamentals, algorithms, AutoML, OML4Py, and deployment, enabling professionals to build, manage, and operationalize ML models efficiently.
90
Minutes
50
Questions
65
Passing Score
$245
Exam Cost
Who Should Take This
Data engineers, database administrators, and data scientists who already work with Oracle Autonomous Database and have a solid foundation in SQL and Python should pursue this exam. It prepares them to design and operationalize ML solutions, leverage AutoML, and integrate advanced OML4Py features, advancing their career as Oracle ML specialists.
What's Covered
1
Oracle ML Fundamentals
2
ML Algorithms and Training
3
AutoML and Model Management
4
OML4Py and Advanced Features
5
Deployment and Operations
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
62 learning goals
1
Oracle ML Fundamentals
2 topics
OML Platform
- Implement Oracle Machine Learning workspace setup on Autonomous Database with notebook creation and user management.
- Design in-database ML workflows using OML4SQL and OML4Py for training models directly on database data without data movement.
- Configure OML notebooks with SQL, PL/SQL, Python, and R interpreters for interactive ML development and exploration.
- Evaluate in-database ML versus external ML platforms to determine the optimal approach for data-intensive workloads.
- Configure OML Platform with appropriate settings and parameters for a production deployment scenario in Oracle Machine Learning on Autonomous Database Specialist.
- Assess OML Platform implementations against best practices to identify gaps and recommend improvements.
- Architect OML Platform solutions with scalability patterns and capacity planning for long-term sustainability.
Data Preparation
- Implement data exploration and profiling using OML notebooks with SQL analytics and Python data visualization.
- Design feature engineering pipelines using SQL transformations, OML data transforms, and PL/SQL functions.
- Configure data partitioning strategies for train/test/validation splits using SQL-based sampling and stratification.
- Analyze data quality and feature importance to select relevant features and address class imbalance for model training.
- Evaluate Data Preparation alternatives and tradeoffs to recommend the optimal approach for given constraints.
- Formulate Data Preparation governance frameworks with policies, standards, and compliance monitoring.
- Explain how to troubleshoot common issues with Data Preparation including error messages and diagnostic procedures.
2
ML Algorithms and Training
2 topics
Classification and Regression
- Design classification model training using OML4SQL with decision tree, random forest, SVM, and neural network algorithms.
- Implement regression models using OML4SQL with linear regression, generalized linear models, and gradient boosted trees.
- Configure model hyperparameters and training settings using ALTER_MODEL and SET_MODEL_SETTING procedures.
- Evaluate classification and regression model performance to select the best algorithm for prediction accuracy and speed.
- Design enterprise-grade Classification and Regression architectures incorporating HA, DR, and security requirements.
- Apply Classification and Regression configuration patterns to meet specific business requirements including compliance needs.
Clustering and Association
- Implement clustering models using OML4SQL with k-means, expectation maximization, and O-Cluster algorithms.
- Design anomaly detection workflows using OML one-class SVM and clustering-based outlier identification.
- Configure association rule mining using Apriori algorithm for market basket analysis and recommendation patterns.
- Analyze clustering results to determine optimal cluster count, validate cluster quality, and interpret segments.
- Implement Clustering and Association following best practices for security, performance, and reliability.
- Diagnose Clustering and Association issues by analyzing metrics, logs, and configuration to determine root causes.
3
AutoML and Model Management
2 topics
AutoML
- Design AutoML workflows using OML AutoML UI for automated algorithm selection, feature engineering, and tuning.
- Implement OML AutoML API for programmatic model selection with metric optimization and constraint configuration.
- Configure AutoML trials with algorithm whitelisting, time budgets, and evaluation metric selection for efficient search.
- Evaluate AutoML results versus manual model development to determine the optimal approach for model quality and development time.
- Analyze AutoML configurations to identify security vulnerabilities, bottlenecks, and optimization opportunities.
- Recommend AutoML optimization strategies balancing performance, cost, and operational complexity.
Model Management
- Architect model lifecycle management with OML model repository, versioning, and deployment for production serving.
- Implement model scoring using SQL PREDICTION functions for real-time inference directly within database queries.
- Configure model export and import using DBMS_DATA_MINING for model portability across Autonomous Database instances.
- Analyze model deployment options to select between in-database scoring, REST endpoints, and batch prediction approaches.
- Architect Model Management solutions with scalability patterns and capacity planning for long-term sustainability.
- Configure Model Management with appropriate settings and parameters for a production deployment scenario in Oracle Machine Learning on Autonomous Database Specialist.
4
OML4Py and Advanced Features
2 topics
OML4Py
- Implement OML4Py for running Python ML workflows with scikit-learn, TensorFlow, and custom algorithms on ADB data.
- Design embedded Python execution using OML4Py table functions for running Python code at database scale.
- Configure OML4Py data store for persisting Python objects, models, and dataframes in the database for sharing and reuse.
- Evaluate OML4Py versus OML4SQL for different ML tasks based on algorithm availability, performance, and development workflow.
- Explain how to troubleshoot common issues with OML4Py including error messages and diagnostic procedures.
- Evaluate OML4Py alternatives and tradeoffs to recommend the optimal approach for given constraints.
Text and Graph Analytics
- Implement Oracle Text features with CTX indexes for full-text search, sentiment analysis, and document classification.
- Design graph analytics workflows using Oracle Property Graph for link analysis, community detection, and path finding.
- Configure OML4SQL text mining algorithms for document clustering, topic modeling, and text classification on ADB.
- Analyze text and graph analytics results to extract actionable insights from unstructured and connected data.
- Compare Text and Graph Analytics deployment patterns to determine the best architecture for availability and scalability needs.
- Design enterprise-grade Text and Graph Analytics architectures incorporating HA, DR, and security requirements.
5
Deployment and Operations
2 topics
REST Deployment
- Design OML REST API deployment for serving ML models with real-time scoring endpoints and batch prediction services.
- Implement OML REST API authentication, endpoint configuration, and scoring request/response handling.
- Configure model A/B testing through REST endpoints for comparing model versions in production environments.
- Evaluate REST deployment performance to optimize latency, throughput, and scaling for production ML serving.
- Plan REST Deployment migration and modernization strategies with phased rollout and rollback procedures.
- Implement REST Deployment following best practices for security, performance, and reliability.
Monitoring and Optimization
- Implement ML model monitoring for prediction drift detection and performance degradation alerting.
- Design model retraining pipelines with scheduled training, evaluation gates, and automated deployment for model refresh.
- Configure OML workspace resource management for CPU, memory, and parallel execution across multiple ML users.
- Analyze ML system performance to identify model staleness, resource contention, and optimization opportunities.
- Deploy Monitoring and Optimization with integration to monitoring, logging, and alerting services for operational visibility.
- Analyze Monitoring and Optimization configurations to identify security vulnerabilities, bottlenecks, and optimization opportunities.
Scope
Included Topics
- All domains in the Oracle Machine Learning on Autonomous Database Specialist certification exam guide.
- Core topics: Oracle ML, In-Database ML, AutoML, OML4SQL, OML4Py, OML Notebooks, Model Deployment, ML Algorithms.
- Oracle services, tools, and best practices relevant to this certification.
- Scenario-based problem solving at the professional level.
Not Covered
- Topics outside the official exam guide scope.
- Programming language specifics beyond the exam requirements.
- Specific pricing values that change over time.
- Third-party products beyond basic integration awareness.
Official Exam Page
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