Exam Professional Machine Learning Engineer topic 1 question 303 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 303
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You work for a media company that operates a streaming movie platform where users can search for movies in a database. The existing search algorithm uses keyword matching to return results. Recently, you have observed an increase in searches using complex semantic queries that include the movies’ metadata such as the actor, genre, and director.

You need to build a revamped search solution that will provide better results, and you need to build this proof of concept as quickly as possible. How should you build the search platform?

A. Use a foundational large language model (LLM) from Model Garden as the search platform’s backend.
B. Configure Vertex AI Vector Search as the search platform’s backend.
C. Use a BERT-based model and host it on a Vertex AI endpoint.
D. Create the search platform through Vertex AI Agent Builder.

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Suggested Answer: B 🗳️

by tk786786 at Feb. 19, 2025, 8:38 p.m.

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5091a99

2 months ago

Selected Answer: B

Answer B. Vector search is more efficient for 'Search' based queries. - A: Makes sense and easily deployable, but this is 'Search' and LLMs typically are for more conversational applications that may not prioritize speed. - C: BERT unnecessary complexity and training. - D: Would work, but Agents are more geared toward conversation and results have higher latency compared to vector search.

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Exam Professional Machine Learning Engineer topic 1 question 303 discussion

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