final-quiz.md

Final Quiz

passing: 75
badge: images/badge.png

Introduction

Use this scored quiz to check your understanding of the Seer Bank Finance LiveStack workshop. The questions connect each finance outcome to the database evidence you inspected in the labs.

Objectives

• Review the main database capabilities used in the workshop.
• Connect each finance outcome to supporting database evidence.
• Earn the workshop badge by answering the scored questions.

Estimated Time: 5 minutes

Task 1: Answer the quiz questions

1. Complete the scored quiz.

   Q: Why does the workshop start by checking the finance data foundation before running later labs?
   * It proves that the shared schema can support risk, operations, prediction, governed answers, and agent actions from one evidence base.
   - It asks each learner to build a separate database before using the Finance LiveStack Demo.
   - It replaces the need to inspect the business outcome in later labs.
   - It moves finance records into external spreadsheets before analysis.
   > The foundation lab ties the business workflow to a single governed database foundation. That makes later dashboard, vector, graph, spatial, OML, copilot, and agent results more explainable.
   
   Q: What is the main business value of recreating dashboard evidence with SQL?
   - It hides the underlying evidence from risk operations users.
   * It lets operators move from a KPI to trusted detail without switching systems or relying on disconnected pipelines.
   - It proves that screenshots are enough for audit review.
   - It removes the need for governed finance views.
   > The dashboard lab is about explainability. SQL aggregates connect the application summary to reviewable signal, exposure, transaction, service, and audit evidence.
   
   Q: Which persona benefit does JSON Relational Duality provide in the transaction lab?
   - Risk teams lose SQL access once the application receives a JSON document.
   - Application teams must copy transaction records into a separate document store.
   * Application developers can serve document-shaped payloads while database teams preserve relational controls.
   - Business users must manually parse JSON strings before reviewing transactions.
   > The business outcome is API-friendly transaction access without sacrificing relational integrity, governance, or SQL projection.
   
   Q: Why is in-database AI Vector Search valuable for risk signal intelligence?
   - It limits analysts to exact keyword matches.
   * It lets analysts search by meaning while keeping source text, embeddings, and scoring inside the governed database.
   - It replaces reviewable SQL with hidden prompt output.
   - It searches only table and column names.
   > The vector lab shows semantic search by intent, not just keywords. The governance value is that embedding and similarity scoring stay near the finance data.
   
   Q: What business problem does the property graph lab solve for fraud investigators?
   - It predicts revenue for finance products.
   * It exposes relationship paths so investigators can explain why accounts, devices, payees, IP addresses, and phones are connected.
   - It replaces audit history with untracked graph output.
   - It stores service coverage regions for operations teams.
   > The graph lab focuses on relationship evidence. A fraud analyst can prioritize connected entities without relying on fragile chains of manual joins.
   
   Q: Why does the service coverage lab use spatial data?
   - To make coverage decisions outside the database.
   * To support location-aware decisions such as nearest service center, demand region pressure, and SLA zone coverage.
   - To hide capacity evidence from service operations leaders.
   - To replace maps and spatial queries with static labels.
   > Spatial data lets operations teams reason about distance and coverage from database evidence, which supports mapping, spatial queries, and location-aware applications.
   
   Q: What outcome does in-database OML scoring support?
   - Sensitive finance records must be exported before each prediction.
   * Risk, revenue, segmentation, and demand predictions can be scored where governed finance records already live.
   - The application UI becomes the only place where model output can be reviewed.
   - Models can be trusted without any SQL evidence.
   > The OML lab is not only about model names. It shows how deployed models produce reviewable predictions close to the data that drives them.
   
   Q: What makes the governed copilot and agent console patterns trustworthy?
   - Hidden prompts and untracked actions.
   - Unapproved tables with no visible SQL.
   * Natural-language answers and agent actions are tied to approved views, visible SQL, controlled tools, and audit records.
   - Browser-only answers that cannot be repeated.
   > The governed AI outcome is reviewability. Business users can ask questions or request actions while technical teams prove what data, SQL, tool, and audit record supported the result.
   

2. Review the completion badge.

   

Acknowledgements

• Author - Pat Shepherd, Senior Principal Database Product Manager
• Contributor - Linda Foinding, Principal Database Product Manager
• Last Updated By/Date - Oracle Database Product Management, June 2026