Oracle Data Platform Foundations Associate (2025) — Lesson 3: Analytics, semantic models, visualization, machine learning, generative AI touchpoints, metadata, data quality, lineage, and cataloging

Learning Objectives

• Break down the roles, prerequisites, records, and decision points involved in Analytics, semantic models, visualization, machine learning, generative AI touchpoints, metadata, data quality, lineage, and cataloging.
• Complete a controlled activity for Analytics, semantic models, visualization, machine learning, generative AI touchpoints, metadata, data quality, lineage, and cataloging and capture governed analytics pathway and lineage evidence.
• Challenge the result with a scenario involving an executive metric tracing to multiple unaligned source definitions, then justify the corrective action.

Detailed Topics

• Analytics
• Semantic models
• Visualization
• Machine learning
• Generative AI touchpoints
• Metadata
• Data quality
• Lineage
• And cataloging
• Decision ownership, prerequisites, and acceptance evidence for Analytics, semantic models, visualization, machine learning, generative AI touchpoints, metadata, data quality, lineage, and cataloging
• Relevant access, data, integration, performance, recovery, and support considerations

Instructor Demonstration

The demonstration traces Analytics, semantic models, visualization, machine learning, generative AI touchpoints, metadata, data quality, lineage, and cataloging from requirement through execution and review. Learners see where evidence should be collected, how governed analytics pathway and lineage evidence supports verification, and which warning signs demand investigation.

Hands-On Exercise

Work through a role-based scenario involving Analytics, semantic models, visualization, machine learning, generative AI touchpoints, metadata, data quality, lineage, and cataloging. Validate the expected path, test the failure case “an executive metric tracing to multiple unaligned source definitions,” choose the safest corrective action, and capture the reasoning and outcome in governed analytics pathway and lineage evidence.

1. Record the starting state, assumptions, identities, data, and expected outcome.
2. Execute the planned workflow and collect evidence at each material checkpoint.
3. Test one valid path, one boundary condition, and the stated failure scenario.
4. Document the correction, retest result, rollback option, and follow-up owner.

Scenario Analysis

• Clarify the business trigger, affected roles, required records, and acceptable result for Analytics, semantic models, visualization, machine learning, generative AI touchpoints, metadata, data quality, lineage, and cataloging.
• Compare at least two implementation or correction choices and identify which evidence would distinguish them.
• Decide how governed analytics pathway and lineage evidence should be reviewed, approved, retained, and handed to the next owner.

Troubleshooting Guidance

When results from Analytics, semantic models, visualization, machine learning, generative AI touchpoints, metadata, data quality, lineage, and cataloging are unexpected, first reproduce the issue with the same role, record, date, and transaction context. Inspect governed analytics pathway and lineage evidence, then test whether “an executive metric tracing to multiple unaligned source definitions” explains the evidence before changing configuration. Retest the corrected path and one unaffected path to guard against regression.

Validation Checklist

• Configuration and source data used for Analytics, semantic models, visualization, machine learning, generative AI touchpoints, metadata, data quality, lineage, and cataloging are versioned or otherwise traceable.
• Governed analytics pathway and lineage evidence shows both the baseline and the accepted result.
• Security, performance, integration, and operational side effects have been considered where relevant.
• Recovery steps and handover notes are usable by a support practitioner.

Lesson Outcome

Learners can apply Analytics, semantic models, visualization, machine learning, generative AI touchpoints, metadata, data quality, lineage, and cataloging, explain the decisions made, and defend the result with governed analytics pathway and lineage evidence.