Professional-Machine-Learning-Engineer Exam Dumps | Your organization's marketing team is building a customer recommendation chatbot that uses a generative

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Question 25/137

Your organization's marketing team is building a customer recommendation chatbot that uses a generative AI large language model (LLM) to provide personalized product suggestions in real time. The chatbot needs to access data from millions of customers, including purchase history, browsing behavior, and preferences. The data is stored in a Cloud SQL for PostgreSQL database. You need the chatbot response time to be less than
100ms. How should you design the system?

A. Use BigQuery ML to fine-tune the LLM with the data in the Cloud SQL for PostgreSQL database, and access the model from BigQuery.

B. Replicate the Cloud SQL for PostgreSQL database to AlloyDB. Configure the chatbot server to query AlloyDB.

C. Transform relevant customer data into vector embeddings and store them in Vertex AI Search for retrieval by the LLM.

D. Create a caching layer between the chatbot and the Cloud SQL for PostgreSQL database to store frequently accessed customer data. Configure the chatbot server to query the cache.

Correct Answer: D

A caching layer is essential to reduce database access time, meeting the <100ms requirement. Caches store high-frequency, low-latency queries in memory, minimizing access delays caused by database lookups. While AlloyDB (Option B) provides performance benefits, a caching layer is more efficient and cost-effective for this purpose. BigQuery ML (Option A) is less ideal for real-time personalized responses due to access speed, and vector embeddings (Option C) are not needed unless semantic search is a requirement.

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