Question 1

A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices. The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet. How should the Generative AI Engineer architect their LLM system?

Options :

A :

Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.

B :

Query the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.

C :

Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime

D :

Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.

Answer: D

Question 2

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.

Options :

A : models:/{model_name}/latest

B : models:/{model_name}@{alias}

C : models:/{model_name}@{version}

D : models:/{model_name}/{model_version}

Answer: B,D

Question 3

In which of the following scenarios model learns using RAG?

Options :

A : Model Fine Tuning

B : None of these options

C : Model Pre-Training

D : Passing Contextual Information

Answer: D

Question 4

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.)

Options :

A : call_cust_history

B : maintenance_schedule

C : call_rep_history

D : call_detail

E : transcript Volume

Answer: D,E

Question 5

As a Generative AI engineer, you are responsible for developing and deploying a machine learning model that provides real-time predictions for a financial services application. The model must be monitored for performance, and you need to track its training metrics, serve it in production, and evaluate its accuracy over time. Which combination of Databricks products would best address these requirements?

Options :

A : Use MLflow Tracking to record and manage the model-s training metrics, Model Serving for deploying the model in production, and Lakehouse Monitoring to monitor the model-s performance and operational metrics.

B : Use Lakehouse Monitoring to evaluate the model-s accuracy, MLflow Tracking to deploy the model, and Model Serving to monitor the model-s performance in production.

C : Use MLflow Evaluation to track the model-s training process, MLflow Tracking for evaluating the model-s accuracy, and Model Serving to deploy the model.

D : Use MLflow Tracking to monitor the model-s real-time performance, MLflow Evaluation for tracking training metrics, and Lakehouse Monitoring to serve the model in production.

Answer: A

Confidently Practice Online with Free Databricks-Certified-Generative-AI-Engineer-Associate Exam Cram

Buying Options: