Become Databricks Certified with updated Databricks-Certified-Generative-AI-Engineer-Associate exam questions and correct answers
A Generative Al Engineer is responsible for developing a chatbot to enable their companys internal HelpDesk Call Center team to more quickly find related tickets and provide resolution. While creating the GenAI application work breakdown tasks for this project, they realize they need to start planning which data sources (either Unity Catalog volume or Delta table) they could choose for this application. They have collected several candidate data sources for consideration: call_rep_history: a Delta table with primary keys representative_id, call_id. This table is maintained to calculate representatives call resolution from fields call_duration and call start_time. transcript Volume: a Unity Catalog Volume of all recordings as a *.wav files, but also a text transcript as *.txt files. call_cust_history: a Delta table with primary keys customer_id, cal1_id. This table is maintained to calculate how much internal customers use the HelpDesk to make sure that the charge back model is consistent with actual service use. call_detail: a Delta table that includes a snapshot of all call details updated hourly. It includes root_cause and resolution fields, but those fields may be empty for calls that are still active. maintenance_schedule “ a Delta table that includes a listing of both HelpDesk application outages as well as planned upcoming maintenance downtimes. They need sources that could add context to best identify ticket root cause and resolution. Which TWO sources do that? (Choose two.)
As a Gen AI engineer, you are preparing to deploy a large application that integrates multiple LLMs and external GenAI services. What is a critical step to ensure the deployment is successful and the application runs smoothly in production?
A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed. Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?
As a Gen AI engineer managing the lifecycle of models in MLflow, which of the following URI patterns are used for referencing models in the Unity Catalog (MLflow) Model Registry? Choose two correct answers.
As a Gen AI engineer tasked with selecting a deployment strategy for an email marketing system that generates customer segmentation reports daily based on accumulated data, which deployment strategy would be most appropriate given the need to process data on a regular schedule and write results to persistent storage?
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