Databricks Generative AI Engineer Associate Dumps Questions – Effective Way to Get Certified

1. A Generative AI Engineer is developing a patient-facing healthcare-focused chatbot. If the patient’s question is not a medical emergency, the chatbot should solicit more information from the patient to pass to the doctor’s office and suggest a few relevant pre-approved medical articles for reading. If the patient’s question is urgent, direct the patient to calling their local emergency services.

Given the following user input:

“I have been experiencing severe headaches and dizziness for the past two days.”

Which response is most appropriate for the chatbot to generate?

2. 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?

3. 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?

4. A Generative Al Engineer wants their (inetuned LLMs in their prod Databncks workspace available for testing in their dev workspace as well. All of their workspaces are Unity Catalog enabled and they are currently logging their models into the Model Registry in MLflow.

What is the most cost-effective and secure option for the Generative Al Engineer to accomplish their gAi?

5. A Generative Al Engineer is developing a RAG application and would like to experiment with

different embedding models to improve the application performance.

Which strategy for picking an embedding model should they choose?

6. A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.

Which set of high level tasks should the Generative AI Engineer's system perform?

7. A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs.

Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?

8. 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?

9. A Generative Al Engineer has developed an LLM application to answer questions about internal

company policies. The Generative AI Engineer must ensure that the application doesn’t hallucinate or leak confidential data.

Which approach should NOT be used to mitigate hallucination or confidential data leakage?

10. A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries.

Which metric should they monitor for their customer service LLM application in production?

11. 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?

12. A Generative Al Engineer is working with a retail company that wants to enhance its customer experience by automatically handling common customer inquiries. They are working on an LLM-powered Al solution that should improve response times while maintaining a personalized interaction. They want to define the appropriate input and LLM task to do this.

Which input/output pair will do this?

13. A Generative Al Engineer is building a system that will answer questions on currently unfolding news topics. As such, it pulls information from a variety of sources including articles and social media posts. They are concerned about toxic posts on social media causing toxic outputs from their system.

Which guardrail will limit toxic outputs?

14. A Generative Al Engineer needs to design an LLM pipeline to conduct multi-stage reasoning that leverages external tools. To be effective at this, the LLM will need to plan and adapt actions while performing complex reasoning tasks.

Which approach will do this?

15. A Generative Al Engineer would like an LLM to generate formatted JSON from emails.

This will require parsing and extracting the following information: order ID, date, and sender email.

Here’s a sample email:





They will need to write a prompt that will extract the relevant information in JSON format with the

highest level of output accuracy.

Which prompt will do that?

16. 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?

17. A Generative Al Engineer is responsible for developing a chatbot to enable their company’s 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 C 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.)

18. What is an effective method to preprocess prompts using custom code before sending them to an LLM?

19. A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they’re willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.

Which model meets all the Generative Al Engineer’s needs in this situation?