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Exam Certified Generative AI Engineer Associate All Questions

View all questions & answers for the Certified Generative AI Engineer Associate exam

Exam Certified Generative AI Engineer Associate topic 1 question 5 discussion

Actual exam question from Databricks's Certified Generative AI Engineer Associate
Question #: 5
Topic #: 1
[All Certified Generative AI Engineer Associate Questions]

A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.
Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?

  • A. DatabricksIQ
  • B. Foundation Model APIs
  • C. Feature Serving
  • D. AutoML
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Suggested Answer: C 🗳️

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trendy01
1 week, 4 days ago
Answer C, B. Foundation Model APIs Description: An API that accesses the LLM model, allowing you to use the model's features, but is not suitable for collecting real-time sports data directly. C.Feature Serving Description: Feature Serving is a function that provides features that a machine learning model can use in real time. This allows real-time sports data (e.g. scores, statistics) to be fed into the model, making it ideal for generating analytics based on this data.
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Harry_D
2 weeks, 5 days ago
I am changing my vote. I think C. Feature Serving is the correct answer. In this post from microsoft, it talks about what is feature serving. https://learn.microsoft.com/en-us/azure/databricks/machine-learning/feature-store/feature-function-serving With Databricks Feature Serving, you can serve structured data for retrieval augmented generation (RAG) applications, as well as features that are required for other applications, such as models served outside of Databricks or any other application that requires features based on data in Unity Catalog. The code provided in https://learn.microsoft.com/en-us/azure/databricks/machine-learning/feature-store/feature-serving-tutorial gives an example of how to use feature serving.
upvoted 1 times
awron_durat
1 week, 5 days ago
I agree! DatabricksIQ and AutoML are more focused on model development and optimization, not real-time data provisioning. Foundation Model APIs provide general pre-trained models for tasks, but don't handle live data integration for near real-time sports commentary.
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Harry_D
3 weeks, 6 days ago
I vote for B. Foundation Model API.
upvoted 1 times
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