Online Access Free Databricks-Generative-AI-Engineer-Associate Practice Test

A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results.
How should they configure the endpoint to pass the secrets and credentials?

• A.Use spark.conf.set ()
• B.Add credentials using environment variables
• C.Pass the secrets in plain text
• D.Pass variables using the Databricks Feature Store API

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.)

• A.Use a smaller embedding model to generate
• B.Retrain the response generating model using ALiBi
• C.Reduce the number of records retrieved from the vector database
• D.Reduce the maximum output tokens of the new model
• E.Decrease the chunk size of embedded documents

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LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.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.Feature Serving
• B.AutoML
• C.Foundation Model APIs
• D.DatabrickslQ

A Generative AI Engineer is creating an agent-based LLM system for their favorite monster truck team. The system can answer text based questions about the monster truck team, lookup event dates via an API call, or query tables on the team's latest standings.
How could the Generative AI Engineer best design these capabilities into their system?

• A.Build a system prompt with all possible event dates and table information in the system prompt. Use a RAG architecture to lookup generic text questions and otherwise leverage the information in the system prompt.
• B.Write a system prompt for the agent listing available tools and bundle it into an agent system that runs a number of calls to solve a query.
• C.Instruct the LLM to respond with "RAG", "API", or "TABLE" depending on the query, then use text parsing and conditional statements to resolve the query.
• D.Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.

When developing an LLM application, it's crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?

• A.Only use data explicitly labeled with an open license and ensure the license terms are followed.
• B.Reach out to the data curators directly after you have started using the trained model to let them know.
• C.Reach out to the data curators directly before you have started using the trained model to let them know.
• D.Use any available data you personally created which is completely original and you can decide what license to use.