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

If you're in the field of Databricks, you know how important it is to stay up-to-date with the latest knowledge and skills to protect your organization's networks and data. One way to do that is by obtaining Generative AI Engineer, specifically the Databricks Generative AI Engineer Associate exam. While preparing for the Databricks Generative AI Engineer Associate exam, you might consider using Databricks Generative AI Engineer Associate dumps to help you familiarize yourself with the exam format and content. These Databricks Generative AI Engineer Associate exam dumps questions can be an effective way to gauge your knowledge and identify areas where you may need additional study. Study online free Databricks Generative AI Engineer Associate exam dumps below.

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1. A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.

Which will fulfill their need?

context length 514; smallest model is 0.44GB and embedding dimension 768

context length 2048: smallest model is 11GB and embedding dimension 2560

context length 32768: smallest model is 14GB and embedding dimension 4096

context length 512: smallest model is 0.13GB and embedding dimension 384

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2. A Generative AI Engineer wants to build an LLM-based solution to help a restaurant improve its online customer experience with bookings by automatically handling common customer inquiries. The goal of the solution is to minimize escalations to human intervention and phone calls while maintaining a personalized interaction. To design the solution, the Generative AI Engineer needs to define the input data to the LLM and the task it should perform.

Which input/output pair will support their goal?

Input: Online chat logs; Output: Group the chat logs by users, followed by summarizing each user’s interactions

Input: Online chat logs; Output: Buttons that represent choices for booking details

Input: Customer reviews; Output: Classify review sentiment

Input: Online chat logs; Output: Cancellation options

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3. 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?

DatabrickslQ

Foundation Model APIs

Feature Serving

AutoML

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4. A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport.

What are the steps needed to build this RAG application and deploy it?

Ingest documents from a source C> Index the documents and saves to Vector Search C> User submits queries against an LLM C> LLM retrieves relevant documents C> Evaluate model C> LLM generates a response C> Deploy it using Model Serving

Ingest documents from a source C> Index the documents and save to Vector Search C> User submits queries against an LLM C> LLM retrieves relevant documents C> LLM generates a response -> Evaluate model C> Deploy it using Model Serving

Ingest documents from a source C> Index the documents and save to Vector Search C> Evaluate model C> Deploy it using Model Serving

User submits queries against an LLM C> Ingest documents from a source C> Index the documents and save to Vector Search C> LLM retrieves relevant documents C> LLM generates a response C> Evaluate model C> Deploy it using Model Serving

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

Directly modify the LLM’s internal architecture to include preprocessing steps

It is better not to introduce custom code to preprocess prompts as the LLM has not been trained with examples of the preprocessed prompts

Rather than preprocessing prompts, it’s more effective to postprocess the LLM outputs to align the outputs to desired outcomes

Write a MLflow PyFunc model that has a separate function to process the prompts

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6. 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?

Dolly 1.5B

OpenAI GPT-4

BGE-large

Llama2-70B

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7. 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?

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

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.

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

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

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8. 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?

Increase the frequency of upstream data updates

Inform the user of the expected RAG behavior

Restrict access to the data sources to a limited number of users

Curate upstream data properly that includes manual review before it is fed into the RAG system

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9. A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users on its platform while its users play online video games.

Which metric would help them increase user engagement and retention for their platform?

Randomness

Diversity of responses

Lack of relevance

Repetition of responses

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10. 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?

Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.

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.

Benchmark multiple LLMs with the same data and pick the best LLM for the job.

Use an LLM-as-a-judge to evaluate the quality of the final answers generated.

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11. A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names.

Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?

Implement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist

Reduce the time that the users can interact with the LLM

Ask the LLM to remind the user that the input is malicious but continue the conversation with the user

Increase the amount of compute that powers the LLM to process input faster

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12. A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. The match should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text.

How should the Generative Al Engineer architect their system?

Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.

Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members’ profiles and perform keyword matching to find the best available team member.

Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.

Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.

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13. 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?

Assess the quality of the retrieved context

Implement caching for frequently asked questions

Use a different LLM to improve the generated response

Use a different semantic similarity search algorithm

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14. 1.A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author’s web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user’s query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.

Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)

Change embedding models and compare performance.

Add a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.

Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters. Choose the strategy that gives the best performance metric.

Pass known questions and best answers to an LLM and instruct the LLM to provide the best token count. Use a summary statistic (mean, median, etc.) of the best token counts to choose chunk size.

Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric.

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