Pinecone Quiz

Answers and explanations appear as you take the quiz.

1. What does Pinecone primarily store and search?
   • Vector embeddings that represent the meaning of data
   • Raw SQL tables of rows and columns
   • Compiled machine learning model weights
   • Encrypted user passwords
2. Why is Pinecone commonly paired with large language models?
   • It trains the language model from scratch
   • It provides external memory so the model can retrieve relevant facts (RAG)
   • It compresses the model to run on phones
   • It translates the model's output into other languages
3. How does Pinecone describe its serverless index algorithms?
   • It uses HNSW for every serverless index
   • Users must manually select one universal algorithm
   • It selects proprietary algorithms such as Ananas, PQFS, or IVF with PQFS by slab size
   • It searches only by exact string match
4. What architecture change does Pinecone highlight in its serverless database?
   • Every vector must be stored in one local in-memory graph
   • Each user must operate a separate physical database
   • Queries are answered without reading or scoring vectors
   • Storage and compute are decoupled so relevant index portions can be loaded for a query
5. How is similarity between two vectors typically measured?
   • By cosine distance or dot product
   • By their file size in megabytes
   • By the date they were created
   • By counting shared exact keywords
6. Which task is Pinecone well suited for?
   • Storing and rendering 3D video game graphics
   • Finding documents similar in meaning to a query
   • Running spreadsheet formulas
   • Compiling source code
7. What is a key step before using retrieved Pinecone matches in a generated answer?
   • Assume the nearest vector is always a correct source
   • Ignore metadata and access controls
   • Evaluate whether the retrieved records are relevant and current for the query
   • Treat retrieval as proof the model’s response is correct
8. What kinds of searchable data do current Pinecone serverless indexes support?
   • Only image files with no metadata
   • Only one fixed embedding model
   • Only SQL tables with no text fields
   • Depending on schema, dense vectors, sparse vectors, and full-text-search fields