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Sharpen Your Knowledge with Databricks (Databricks Certified Generative AI Engineer Associate) Certification Sample Questions
CertsTime has provided you with a sample question set to elevate your knowledge about the Databricks Certified Generative AI Engineer Associate exam. With these updated sample questions, you can become quite familiar with the difficulty level and format of the real Databricks Certified Generative AI Engineer Associate certification test. Try our sample Databricks Certified Generative AI Engineer Associate certification practice exam to get a feel for the real exam environment. Our sample practice exam gives you a sense of reality and an idea of the questions on the actual Databricks Generative AI Engineer Associate certification exam.
Our sample questions are similar to the Real Databricks Certified Generative AI Engineer Associate exam questions. The premium Databricks Certified Generative AI Engineer Associate certification practice exam gives you a golden opportunity to evaluate and strengthen your preparation with real-time scenario-based questions. Plus, by practicing real-time scenario-based questions, you will run into a variety of challenges that will push you to enhance your knowledge and skills.
Databricks Certified Generative AI Engineer Associate Sample Questions:
A Generative AI Engineer has created a RAG application which can help employees retrieve answers from an internal knowledge base, such as Confluence pages or Google Drive. The prototype application is now working with some positive feedback from internal company testers. Now the Generative Al Engineer wants to formally evaluate the system's performance and understand where to focus their efforts to further improve the system.
How should the Generative AI Engineer evaluate the system?
A Generative Al Engineer has successfully ingested unstructured documents and chunked them by document sections. They would like to store the chunks in a Vector Search index. The current format of the dataframe has two columns: (i) original document file name (ii) an array of text chunks for each document.
What is the most performant way to store this dataframe?
A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.
The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.
Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?
A Generative Al Engineer is building a system which will answer questions on latest stock news articles.
Which will NOT help with ensuring the outputs are relevant to financial news?
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:
What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)
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