Data funamentals v1.5

data-fundamentals-dev-rel/quiz/quiz.md

@@ -18,12 +18,6 @@ Estimated Time: 5 minutes
 ### Quiz Questions
 
 ```quiz score
-Q: What is the primary purpose of RAG (Retrieval-Augmented Generation)?
-- To replace the need for a database entirely
-- To build a better augmented
-* To retrieve and attach physical hardware components to an LLM, thus making your own ghost in the shell. Major Kusanagi would be so proud
-* To augment an LLM's responses by retrieving and providing relevant data that exists outside its training knowledge
-
 Q: True or False: Oracle AI Database enables you to create vector embeddings directly within the database.
 * True
 - False
@@ -51,7 +45,7 @@ Q: What's likely the best data type to store embeddings in Oracle AI Database?
 Q: What does the VECTOR_CHUNKS function do?
 - It's a hidden function that creates fresh, chunky dog food ondemand
 - Encrypts sensitive customer data
-* Splits text into smaller chunks to generate vector embeddings that can be used with vector indexes or hybrid vector indexes.
+* Splits data into smaller chunks to generate vector embeddings that can be used with vector indexes or hybrid vector indexes
 - Creates backup copies of database tables in bite-sized chunks
 
 Q: Which database feature combines data from one or more relational tables, but projects the data as a JSON document?
@@ -81,4 +75,4 @@ Q: How does Cosine similarity measure distance?
 
 ## Acknowledgements
 * **Authors** - Kirk Kirkconnell
-* **Last Updated By/Date** - Kirk Kirkconnell, January 2026
+* **Last Updated By/Date** - Kirk Kirkconnell, June 2026

dev-rel-rag-to-agents/quiz/quiz.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-Test your knowledge of building a RAG and agentic! This quiz covers key concepts from the lab including Vector Search, embeddings, Python integration, and AI-powered recommendation systems.
+Test your knowledge of building a RAG app and agents! This quiz covers key concepts from the lab including Vector Search, embeddings, Python integration, and AI-powered recommendation systems.
 
 Estimated Time: 5 minutes
 
@@ -42,33 +42,33 @@ Q: What is the key tradeoff of a LLM-driven workflow?
 Q: Why do tool signatures and docstrings matter?
 - They change the physical database schema used by the tool
 * They define the model-facing interface that helps the LLM decide when and how to call a tool
-- They automatically validate every SQL result returned by Oracle
+- They automatically validate every SQL result returned by the database
 - They prevent the framework from executing tool calls
 > The LLM sees the tool name, inputs, and description, so clear interfaces improve tool selection and usage.
 
 Q: What makes the unified query important?
 - It moves data into a separate vector database for faster retrieval
 - It asks the LLM to generate SQL without constraints
 - It replaces the need for asset metadata
-* It combines relational, JSON, graph, and vector evidence in one Oracle-backed SQL call
+* It combines relational, JSON, graph, and vector evidence in one SQL call
 > The unified query shows how Oracle AI Database can provide rich incident context without, CDC, synchronization, ETL, or separate data stores.
 
 Q: What role does LangGraph play in the lab?
-- It creates the original PRISM seed data
+- It makes the code look like an Instagram influencer
 - It replaces Ollama as the LLM runtime
 * It implements the agent reasoning loop where the model can answer or request allowed tool calls
 - It converts markdown quiz blocks into notebook cells
 > LangGraph provides the state graph, tool loop, checkpointing, and memory wiring used by the agent.
 
 Q: Why does the notebook use both short-term and long-term memory for the agent?
 * Short-term memory preserves the current thread, while long-term memory stores durable recallable knowledge across runs
-- Short-term memory stores SQL tables, while long-term memory stores only Python variables
+- Short-term memory stores full SQL tables, while long-term memory stores only Python variables
 - Short-term memory replaces retrieval, while long-term memory disables tool calls
-- Both memories are temporary and disappear when the notebook kernel stops
+- Both memories are temporary and disappear when you close the Jupyter notebook
 > The notebook separates conversation state from persistent semantic memory so the agent can continue a thread and recall prior incident decisions.
 
 ```
 
 ## Acknowledgements
 * **Authors** - Kirk Kirkconnell
-* **Last Updated By/Date** - Kirk Kirkconnell, January 2026
+* **Last Updated By/Date** - Kirk Kirkconnell, July 2026