Multimodal RAG Quiz

Answers and explanations appear as you take the quiz.

1. What typically goes wrong when a text-only RAG pipeline processes a PDF full of tables and charts?
   • The PDF becomes too small to index
   • Tables become jumbled and chart values or diagrams are lost
   • The embeddings become images
   • The model refuses to answer
2. What is the main weakness of the caption-everything-into-text approach?
   • It cannot use existing text embeddings
   • It requires a vector database for images
   • It only works for scanned forms
   • Anything the captioner fails to describe is lost to retrieval
3. What do CLIP-style models make possible in multimodal RAG?
   • Embedding images and text in a shared vector space so text queries can retrieve images
   • Reading dense small text on scanned pages with high accuracy
   • Compressing PDFs before storage
   • Translating captions into other languages
4. What is distinctive about ColPali's approach?
   • It converts every page to Markdown with OCR first
   • It only indexes image captions
   • It embeds page screenshots with a vision-language model and skips OCR and layout parsing
   • It stores one vector per document
5. In late interaction, how is a query scored against a page?
   • One query vector is compared with one page vector
   • Pages are ranked by file size
   • Keyword counts are compared
   • Many query-token vectors are compared with many image-patch vectors
6. Why are ColPali-style indexes much larger than text-chunk indexes?
   • They store the original PDF files inside each vector
   • Each page gets roughly one vector per image patch instead of a single vector
   • They duplicate every page in several languages
   • They require storing the full model weights per page
7. Why pass the page image, not only extracted text, to the generating model?
   • Questions about chart values or visual layout need the model to see the visual itself
   • Images are always cheaper than text
   • Text cannot be cited
   • Generators cannot read text
8. Which statement about multimodal RAG in practice is accurate?
   • It always replaces text pipelines entirely
   • It only works on photographs
   • Hybrid systems combining parsed text, table extraction and page-image retrieval are common
   • It requires no evaluation