Why Dumpsforsure is the best choice for Databricks Databricks-Generative-AI-Engineer-Associate exam preparation?
Secure your position in Highly Competitive IT Industry:
Databricks Databricks-Generative-AI-Engineer-Associate exam certification is the best way to demonstrate your understanding, capability and talent. DumpsforSure is here to provide you with best knowledge on Databricks-Generative-AI-Engineer-Associate certification. By using our Databricks-Generative-AI-Engineer-Associate questions & answers you can not only secure your current position but also expedite your growth process.
Verified by IT and Industry Experts:
We are devoted and dedicated to providing you with real and updated Databricks-Generative-AI-Engineer-Associate exam dumps, along with explanations. Keeping in view the value of your money and time, all the questions and answers on Dumpsforsure has been verified by Databricks experts. They are highly qualified individuals having many years of professional experience.
Ultimate preparation Source:
Dumpsforsure is a central tool to help you prepare your Databricks Databricks-Generative-AI-Engineer-Associate exam. We have collected real exam questions & answers which are updated and reviewed by professional experts regularly. In order to assist you understanding the logic and pass the Databricks exams, our experts added explanation to the questions.
Instant Access to the Real and Updated Databricks Databricks-Generative-AI-Engineer-Associate Questions & Answers:
Dumpsforsure is committed to update the exam databases on regular basis to add the latest questions & answers. For your convenience we have added the date on the exam page showing the most latest update. Getting latest exam questions you'll be able to pass your Databricks Databricks-Generative-AI-Engineer-Associate exam in first attempt easily.
Free Databricks-Generative-AI-Engineer-Associate Dumps DEMO before Purchase:
Dumpsforsure is offering free Demo facility for our valued customers. You can view Dumpsforsure's content by downloading Databricks-Generative-AI-Engineer-Associate free Demo before buying. It'll help you getting the pattern of the exam and form of Databricks-Generative-AI-Engineer-Associate dumps questions and answers.
Three Months Free Updates:
Our professional expert's team is constantly checking for the updates. You are eligible to get 90 days free updates after purchasing Databricks-Generative-AI-Engineer-Associate exam. If there will be any update found our team will notify you at earliest and provide you with the latest PDF file.
SAMPLE QUESTIONS
Question # 1
A Generative Al Engineer is tasked with developing an application that is based on an open sourcelarge language model (LLM). They need a foundation LLM with a large context window.Which model fits this need
A. DistilBERT B. MPT-30B C. Llama2-70B D. DBRX
Answer: C Explanation:Problem Context: The engineer needs an open-source LLM with a large context window to develop an application.Explanation of Options:Option A: DistilBERT: While an efficient and smaller version of BERT, DistilBERT does not provide a particularly large context window.Option B: MPT-30B: This model, while large, is not specified as being particularly notable for itscontext window capabilities.Option C: Llama2-70B: Known for its large model size and extensive capabilities, including a largecontext window. It is also available as an open-source model, making it ideal for applicationsrequiring extensive contextual understanding.Option D: DBRX: This is not a recognized standard model in the context of large language models with extensive context windows.Thus, Option C (Llama2-70B) is the best fit as it meets the criteria of having a large context windowand being available for open-source use, suitable for developing robust language understanding applications
Question # 2
A Generative AI Engineer received the following business requirements for an external chatbot.The chatbot needs to know what types of questions the user asks and routes to appropriate modelsto answer the questions. For example, the user might ask about upcoming event details. Anotheruser might ask about purchasing tickets for a particular event.What is an ideal workflow for such a chatbot?
A. The chatbot should only look at previous event information B. There should be two different chatbots handling different types of user queries. C. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type ofquestion asked, then route the question to the appropriate model. If its an upcoming eventquestion, send the query to a text-to-SQL model. If its about ticket purchasing, the customer shouldbe redirected to a payment platform. D. The chatbot should only process payments
Answer: C Explanation:Problem Context: The chatbot must handle various types of queries and intelligently route them tothe appropriate responses or systems.Explanation of Options:Option A: Limiting the chatbot to only previous event information restricts its utility and does notmeet the broader business requirements.Option B: Having two separate chatbots could unnecessarily complicate user interaction and increasemaintenance overhead.Option C: Implementing a multi-step workflow where the chatbot first identifies the type of questionand then routes it accordingly is the most efficient and scalable solution. This approach allows thechatbot to handle a variety of queries dynamically, improving user experience and operational efficiency.Option D: Focusing solely on payments would not satisfy all the specified user interaction needs,such as inquiring about event details.Option C offers a comprehensive workflow that maximizes the chatbots utility and responsiveness todifferent user needs, aligning perfectly with the business requirements.
Question # 3
A Generative AI Engineer is building a RAG application that will rely on context retrieved from sourcedocuments that are currently in PDF format. These PDFs can contain both text and images. They wantto develop a solution using the least amount of lines of code.Which Python package should be used to extract the text from the source documents?
A. flask B. beautifulsoup C. unstructured D. numpy
Answer: C Explanation:Problem Context: The engineer needs to extract text from PDF documents, which may contain bothtext and images. The goal is to find a Python package that simplifies this task using the least amountof code.Explanation of Options:Option A: flask: Flask is a web framework for Python, not suitable for processing or extracting contentfrom PDFs.Option B: beautifulsoup: Beautiful Soup is designed for parsing HTML and XML documents, not PDFs.Option C: unstructured: This Python package is specifically designed to work with unstructured data,including extracting text from PDFs. It provides functionalities to handle various types of content indocuments with minimal coding, making it ideal for the task.Option D: numpy: Numpy is a powerful library for numerical computing in Python and does notprovide any tools for text extraction from PDFs. Given the requirement, Option C (unstructured) is the most appropriate as it directly addresses theneed to efficiently extract text from PDF documents with minimal code.
Question # 4
A team wants to serve a code generation model as an assistant for their software developers. Itshould support multiple programming languages. Quality is the primary objective.Which of the Databricks Foundation Model APIs, or models available in the Marketplace, would b the best fit?
A. Llama2-70b B. BGE-large C. MPT-7b D. CodeLlama-34B
Answer: D Explanation:For a code generation model that supports multiple programming languages and where quality is theprimary objective, CodeLlama-34B is the most suitable choice. Heres the reasoning:Specialization in Code Generation:CodeLlama-34B is specifically designed for code generation tasks. This model has been trained with afocus on understanding and generating code, which makes it particularly adept at handling variousprogramming languages and coding contexts.Capacity and Performance:The "34B" indicates a model size of 34 billion parameters, suggesting a high capacity for handlingcomplex tasks and generating high-quality outputs. The large model size typically correlates withbetter understanding and generation capabilities in diverse scenarios.Suitability for Development Teams:Given that the model is optimized for code, it will be able to assist software developers moreeffectively than general-purpose models. It understands coding syntax, semantics, and the nuancesof different programming languages.Why Other Options Are Less Suitable:A (Llama2-70b): While also a large model, it's more general-purpose and may not be as fine-tunedfor code generation as CodeLlama.B (BGE-large): This model may not specifically focus on code generation.C (MPT-7b): Smaller than CodeLlama-34B and likely less capable in handling complex codegeneration tasks at high quality.Therefore, for a high-quality, multi-language code generation application, CodeLlama-34B (option D)is the best fit.
Question # 5
A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users onits platform while its users play online video games.Which metric would help them increase user engagement and retention for their platform?
A. Randomness B. Diversity of responses C. Lack of relevance D. Repetition of responses
Answer: B Explanation:In the context of designing a chatbot to engage users on a gaming platform, diversity of responses(option B) is a key metric to increase user engagement and retention. Heres why:Diverse and Engaging Interactions:A chatbot that provides varied and interesting responses will keep users engaged, especially in aninteractive environment like a gaming platform. Gamers typically enjoy dynamic and evolvingconversations, and diversity of responses helps prevent monotony, encouraging users to interactmore frequently with the bot.Increasing Retention:By offering different types of responses to similar queries, the chatbot can create a sense of noveltyand excitement, which enhances the users experience and makes them more likely to return to theplatform.Why Other Options Are Less Effective:A (Randomness): Random responses can be confusing or irrelevant, leading to frustration andreducing engagement.C (Lack of Relevance): If responses are not relevant to the users queries, this will degrade the userexperience and lead to disengagement.D (Repetition of Responses): Repetitive responses can quickly bore users, making the chatbot feeluninteresting and reducing the likelihood of continued interaction.Thus, diversity of responses (option B) is the most effective way to keep users engaged and retainthem on the platform.
Question # 6
A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-datenews articles and stock prices.The design requires the use of stock prices which are stored in Delta tables and finding the latestrelevant news articles by searching the internet.How should the Generative AI Engineer architect their LLM system?
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 toinvestigate potential causes of the stock volatility. C. Download and store news articles and stock price information in a vector store. Use a RAGarchitecture to retrieve and generate at runtime. D. Create an agent with tools for SQL querying of Delta tables and web searching, provide retrievedvalues to an LLM for generation of response.
Answer: D Explanation:To build an LLM-powered system that accesses up-to-date news articles and stock prices, the bestapproach is to create an agent that has access to specific tools (option D).Agent with SQL and Web Search Capabilities:By using an agent-based architecture, the LLM can interact with external tools. The agent can queryDelta tables (for up-to-date stock prices) via SQL and perform web searches to retrieve the latestnews articles. This modular approach ensures the system can access both structured (stock prices)and unstructured (news) data sources dynamically.Why This Approach Works:SQL Queries for Stock Prices: Delta tables store stock prices, which the agent can query directly forthe latest data.Web Search for News: For news articles, the agent can generate search queries and retrieve the mostrelevant and recent articles, then pass them to the LLM for processing.Why Other Options Are Less Suitable:A (Summarizing News for Stock Prices): This convoluted approach would not ensure accuracy whenretrieving stock prices, which are already structured and stored in Delta tables.B (Stock Price Volatility Queries): While this could retrieve relevant information, it doesn't addresshow to obtain the most up-to-date news articles.C (Vector Store): Storing news articles and stock prices in a vector store might not capture the realtimenature of stock data and news updates, as it relies on pre-existing data rather than dynamicquerying.Thus, using an agent with access to both SQL for querying stock prices and web search for retrievingnews articles is the best approach for ensuring up-to-date and accurate responses.
Question # 7
A Generative AI Engineer is building an LLM to generate article summaries in the form of a type ofpoem, such as a haiku, given the article content. However, the initial output from the LLM does notmatch the desired tone or style.Which approach will NOT improve the LLMs response to achieve the desired response?
A. Provide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style B. Use a neutralizer to normalize the tone and style of the underlying documents C. Include few-shot examples in the prompt to the LLM D. Fine-tune the LLM on a dataset of desired tone and style
Answer: B Explanation:The task at hand is to improve the LLMs ability to generate poem-like article summaries with thedesired tone and style. Using a neutralizer to normalize the tone and style of the underlyingdocuments (option B) will not help improve the LLMs ability to generate the desired poetic style.Heres why:Neutralizing Underlying Documents:A neutralizer aims to reduce or standardize the tone of input data. However, this contradicts the goal,which is to generate text with a specific tone and style (like haikus). Neutralizing the sourcedocuments will strip away the richness of the content, making it harder for the LLM to generatecreative, stylistic outputs like poems.Why Other Options Improve Results:A (Explicit Instructions in the Prompt): Directly instructing the LLM to generate text in a specific toneand style helps align the output with the desired format (e.g., haikus). This is a common and effectivetechnique in prompt engineering.C (Few-shot Examples): Providing examples of the desired output format helps the LLM understandthe expected tone and structure, making it easier to generate similar outputs.D (Fine-tuning the LLM): Fine-tuning the model on a dataset that contains examples of the desiredtone and style is a powerful way to improve the models ability to generate outputs that match thetarget format.Therefore, using a neutralizer (option B) is not an effective method for achieving the goal ofgenerating stylized poetic summaries.
Question # 8
A Generative AI Engineer developed an LLM application using the provisioned throughputFoundation Model API. Now that the application is ready to be deployed, they realize their volume ofrequests are not sufficiently high enough to create their own provisioned throughput endpoint. Theywant to choose a strategy that ensures the best cost-effectiveness for their application.What strategy should the Generative AI Engineer use?
A. Switch to using External Models instead B. Deploy the model using pay-per-token throughput as it comes with cost guarantees C. Change to a model with a fewer number of parameters in order to reduce hardware constraintissues D. Throttle the incoming batch of requests manually to avoid rate limiting issues
Answer: B Explanation:Problem Context: The engineer needs a cost-effective deployment strategy for an LLM applicationwith relatively low request volume.Explanation of Options:Option A: Switching to external models may not provide the required control or integrationnecessary for specific application needs.Option B: Using a pay-per-token model is cost-effective, especially for applications with variable orlow request volumes, as it aligns costs directly with usage.Option C: Changing to a model with fewer parameters could reduce costs, but might also impact theperformance and capabilities of the application.Option D: Manually throttling requests is a less efficient and potentially error-prone strategy formanaging costs.Option B is ideal, offering flexibility and cost control, aligning expenses directly with the application'susage patterns.
Question # 9
A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to bedeployed.Which of the following steps correctly outlines the easiest process for deploying a model onDatabricks?
A. Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to UnityCatalog using MLflow, and start a serving endpoint B. Log the model using MLflow during training, directly register the model to Unity Catalog using theMLflow API, and start a serving endpoint C. Save the model along with its dependencies in a local directory, build the Docker image, and runthe Docker container D. Wrap the LLMs prediction function into a Flask application and serve using Gunicorn
Answer: B Explanation:Problem Context: The goal is to deploy a trained LLM on Databricks in the simplest and mostintegrated manner.Explanation of Options:Option A: This method involves unnecessary steps like logging the model as a pickle object, which isnot the most efficient path in a Databricks environment.Option B: Logging the model with MLflow during training and then using MLflows API to register andstart serving the model is straightforward and leverages Databricks built-in functionalities forseamless model deployment.Option C: Building and running a Docker container is a complex and less integrated approach withinthe Databricks ecosystem.Option D: Using Flask and Gunicorn is a more manual approach and less integrated compared to thenative capabilities of Databricks and MLflow.Option B provides the most straightforward and efficient process, utilizing Databricks ecosystem toits full advantage for deploying models
Question # 10
A Generative AI Engineer has created a RAG application which can help employees retrieve answersfrom an internal knowledge base, such as Confluence pages or Google Drive. The prototypeapplication is now working with some positive feedback from internal company testers. Now theGenerative Al Engineer wants to formally evaluate the systems performance and understand whereto 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 generatedanswers. B. Curate a dataset that can test the retrieval and generation components of the system separately.Use MLflows built in evaluation metrics to perform the evaluation on the retrieval and generationcomponents. 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.
Answer: B Explanation:Problem Context: After receiving positive feedback for the RAG application prototype, the next stepis to formally evaluate the system to pinpoint areas for improvement.Explanation of Options:Option A: While cosine similarity scores are useful, they primarily measure similarity rather than theoverall performance of an RAG system.Option B: This option provides a systematic approach to evaluation by testing both retrieval andgeneration components separately. This allows for targeted improvements and a clear understandingof each component's performance, using MLflows metrics for a structured and standardizedassessment.Option C: Benchmarking multiple LLMs does not focus on evaluating the existing systemscomponents but rather on comparing different models.Option D: Using an LLM as a judge is subjective and less reliable for systematic performance evaluation.Option B is the most comprehensive and structured approach, facilitating precise evaluations andimprovements on specific components of the RAG system
