Crucial Exams

GCP Professional Data Engineer Practice Question

A retail media team ingests product descriptions, reviews, and support-chat transcripts into BigQuery. They need to power a retrieval-augmented generation (RAG) service that answers natural-language questions about product issues. Design goals include:

  • The LLM must receive only the top 10 semantically closest text chunks, each ≤ 2 KB.
  • Embeddings should use a managed, up-to-date model without exporting data outside Google Cloud.
  • Monthly ingestion adds 20 million new chunks; query latency must stay below 500 ms.

Which architecture best meets the goals while minimizing operational overhead?

  • Generate embeddings with a custom TensorFlow container on Vertex AI Pipelines, store them in Firestore, and run similarity queries with a Cloud Run microservice that implements HNSW.

  • Use ML.GENERATE_EMBEDDING to write vectors into a BigQuery table clustered on the embedding column, and issue VECTOR_SEARCH queries at read time to retrieve the 10 nearest chunks for the prompt sent to the LLM.

  • Export the text as JSON to Cloud Storage, use the open-source FAISS library on a GKE Autopilot cluster for indexing and ANN search, and stream the results back into BigQuery before calling the LLM.

  • Store the text in a Bigtable row for each chunk, use Dataproc Serverless with Spark MLlib to build word2vec embeddings, and push the top 10 matches into a BI Engine cache that the LLM queries.

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