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Databricks-Generative-AI-Engineer-Associate Practice Questions

Question # 1
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?
A. Dolly 1.5B
B. OpenAI GPT-4
C. BGE-large
D. Llama2-70B



Question # 2
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. Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.
B. Curate a dataset that can test the retrieval and generation components of the system separately. Use MLflow’s built in evaluation metrics to perform the evaluation on the retrieval and generation components.
C. Benchmark multiple LLMs with the same data and pick the best LLM for the job.
D. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.



Question # 3
Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product. What can the engineer do to improve the relevance of the RAG’s response?
A. Assess the quality of the retrieved context
B. Implement caching for frequently asked questions
C. Use a different LLM to improve the generated response
D. Use a different semantic similarity search algorithm



Question # 4
A Generative Al Engineer is tasked with improving the RAG quality by addressing its inflammatory outputs. Which action would be most effective in mitigating the problem of offensive text outputs?

A. Increase the frequency of upstream data updates
B. Inform the user of the expected RAG behavior
C. Restrict access to the data sources to a limited number of users
D. Curate upstream data properly that includes manual review before it is fed into the RAG system



Question # 5
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?

A. Add guardrails to filter outputs from the LLM before it is shown to the user
B. Fine-tune the model on your data, hoping it will learn what is appropriate and not
C. Limit the data available based on the user’s access level
D. Use a strong system prompt to ensure the model aligns with your needs.



Question # 6
A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot’s focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message:
“Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance.”
Which framework type should be implemented to solve this?
A. Safety Guardrail
B. Security Guardrail
C. Contextual Guardrail
D. Compliance Guardrail



Question # 7
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?
A. Implement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.
B. Increase the compute to improve processing speed of questions to allow greater relevancy analysis
C. Incorporate manual reviews to correct any problematic outputs prior to sending to the users
D. Implement a profanity filter to screen out offensive language



Question # 8
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?

A. Number of customer inquiries processed per unit of time
B. Energy usage per query
C. Final perplexity scores for the training of the model
D. HuggingFace Leaderboard values for the base LLM



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Databricks Databricks-Generative-AI-Engineer-Associate Exam Dumps

Exam Name: Databricks Certified Generative AI Engineer Associate
Certification Name: Generative AI Engineer

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  • Total Questions: 45
  • Last Updation Date: 28-Mar-2025

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