Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A developer needs to use the Vertex AI PaLM API to generate text embeddings for a large corpus of documents. Which model should they use?
⚠ Common exam trap
A common mix-up: candidates confuse general-purpose text generation models (like `text-bison@001`) with embedding models, assuming any 'text' model can produce embeddings, but only models with 'embedding' in the name are designed for that purpose.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
textembedding-gecko@001
`textembedding-gecko@001` is the specific Vertex AI model designed for generating text embeddings, which convert text into dense vector representations. This model is optimized for semantic similarity, clustering, and retrieval tasks, making it ideal for processing a large corpus of documents. The other models are designed for code generation, text generation, or chat, not embeddings.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
codey-bison@001
Why it's wrong here
codey-bison@001 is tuned for code generation and completion, so it emits source code rather than embedding vectors for documents. It tempts because it is a PaLM family model handling text, yet its code-specialised training does not produce the dense representations that textembedding-gecko@001 generates for a document corpus.
- ✓
textembedding-gecko@001
Why this is correct
textembedding-gecko@001 is the PaLM-family embedding model on Vertex AI, purpose-built to convert text into dense vectors. It satisfies the embedding requirement for a large corpus, unlike generative text models such as text-bison, which produce completions rather than embeddings.
- ✗
text-bison@001
Why it's wrong here
text-bison@001 is a text-generation model, so it produces completions rather than the fixed-length embedding vectors the corpus requires. It appeals because it processes text, but generation and embedding are distinct tasks; the embedding task needs textembedding-gecko@001, which outputs vectors for similarity and retrieval.
- ✗
chat-bison@001
Why it's wrong here
chat-bison@001 is a conversational model tuned for multi-turn dialogue, so it returns chat responses rather than embedding vectors. It is tempting because it handles natural-language interaction well, but that capability suits building chatbots, not producing the dense vector representations that textembedding-gecko@001 supplies for a document corpus.
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