Databricks Generative AI Engineer Associate Exam Dumps Questions

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<p>Databricks Generative AI Engineer Associate Databricks Certified Generative AI</p><p>Engineer Associate exam dumps questions are the best material for you to test</p><p>all the related Databricks exam topics. By using the Databricks Generative AI</p><p>Engineer Associate exam dumps questions and practicing your skills, you can</p><p>increase your confidence and chances of passing the Databricks Generative AI</p><p>Engineer Associate exam.</p><p>Features of Dumpsinfo’s products</p><p>Instant Download</p><p>Free Update in 3 Months</p><p>Money back guarantee</p><p>PDF and Software</p><p>24/7 Customer Support</p><p>Besides, Dumpsinfo also provides unlimited access. You can get all</p><p>Dumpsinfo files at lowest price.</p><p>Databricks Certified Generative AI Engineer Associate Databricks</p><p>Generative AI Engineer Associate exam free dumps questions are available</p><p>below for you to study.</p><p>Full version: Databricks Generative AI Engineer Associate Exam Dumps</p><p>Questions</p><p>1.A Generative AI Engineer is testing a simple prompt template in LangChain using the code below,</p><p>but is getting an error.</p><p>1 / 8</p><p>https://www.dumpsinfo.com/unlimited-access/</p><p>https://www.dumpsinfo.com/exam/databricks-generative-ai-engineer-associate</p><p>https://www.dumpsinfo.com/exam/databricks-generative-ai-engineer-associate</p><p>Assuming the API key was properly defined, what change does the Generative AI Engineer need to</p><p>make to fix their chain?</p><p>A)</p><p>B)</p><p>2 / 8</p><p>https://www.dumpsinfo.com/</p><p>C)</p><p>D)</p><p>A. Option A</p><p>3 / 8</p><p>https://www.dumpsinfo.com/</p><p>B. Option B</p><p>C. Option C</p><p>D. Option D</p><p>Answer: C</p><p>Explanation:</p><p>To fix the error in the LangChain code provided for using a simple prompt template, the correct</p><p>approach is Option C.</p><p>Here's a detailed breakdown of why Option C is the right choice and how it addresses the issue:</p><p>Proper Initialization: In Option C, the LLMChain is correctly initialized with the LLM instance specified</p><p>as OpenAI(), which likely represents a language model (like GPT) from OpenAI. This is crucial as it</p><p>specifies which model to use for generating responses.</p><p>Correct Use of Classes and Methods:</p><p>The PromptTemplate is defined with the correct format, specifying that adjective is a variable within</p><p>the template. This allows dynamic insertion of values into the template when generating text.</p><p>The prompt variable is properly linked with the PromptTemplate, and the final template string is</p><p>passed correctly.</p><p>The LLMChain correctly references the prompt and the initialized OpenAI() instance, ensuring that the</p><p>template and the model are properly linked for generating output.</p><p>Why Other Options Are Incorrect:</p><p>Option A: Misuses the parameter passing in generate method by incorrectly structuring the dictionary.</p><p>Option B: Incorrectly uses prompt.format method which does not exist in the context of LLMChain and</p><p>PromptTemplate configuration, resulting in potential errors.</p><p>Option D: Incorrect order and setup in the initialization parameters for LLMChain, which would likely</p><p>lead to a failure in recognizing the correct configuration for prompt and LLM usage.</p><p>Thus, Option C is correct because it ensures that the LangChain components are correctly set up and</p><p>integrated, adhering to proper syntax and logical flow required by LangChain's architecture. This</p><p>setup avoids common pitfalls such as type errors or method misuses, which are evident in other</p><p>options.</p><p>2.A Generative AI Engineer developed an LLM application using the provisioned throughput</p><p>Foundation Model API. Now that the application is ready to be deployed, they realize their volume of</p><p>requests are not sufficiently high enough to create their own provisioned throughput endpoint. They</p><p>want to choose a strategy that ensures the best cost-effectiveness for their application.</p><p>What strategy should the Generative AI Engineer use?</p><p>A. Switch to using External Models instead</p><p>B. Deploy the model using pay-per-token throughput as it comes with cost guarantees</p><p>C. Change to a model with a fewer number of parameters in order to reduce hardware constraint</p><p>issues</p><p>D. Throttle the incoming batch of requests manually to avoid rate limiting issues</p><p>Answer: B</p><p>Explanation:</p><p>Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application</p><p>with relatively low request volume.</p><p>Explanation of Options:</p><p>Option A: Switching to external models may not provide the required control or integration necessary</p><p>for specific application needs.</p><p>Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or</p><p>low request volumes, as it aligns costs directly with usage.</p><p>Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the</p><p>performance and capabilities of the application.</p><p>Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for</p><p>4 / 8</p><p>https://www.dumpsinfo.com/</p><p>managing costs.</p><p>Option B is ideal, offering flexibility and cost control, aligning expenses directly with the application's</p><p>usage patterns.</p><p>3.A Generative AI Engineer is developing a patient-facing healthcare-focused chatbot. If the patient’s</p><p>question is not a medical emergency, the chatbot should solicit more information from the patient to</p><p>pass to the doctor’s office and suggest a few relevant pre-approved medical articles for reading. If</p><p>the patient’s question is urgent, direct the patient to calling their local emergency services.</p><p>Given the following user input:</p><p>“I have been experiencing severe headaches and dizziness for the past two days.”</p><p>Which response is most appropriate for the chatbot to generate?</p><p>A. Here are a few relevant articles for your browsing. Let me know if you have questions after reading</p><p>them.</p><p>B. Please call your local emergency services.</p><p>C. Headaches can be tough. Hope you feel better soon!</p><p>D. Please provide your age, recent activities, and any other symptoms you have noticed along with</p><p>your headaches and dizziness.</p><p>Answer: B</p><p>Explanation:</p><p>Problem Context: The task is to design responses for a healthcare-focused chatbot that appropriately</p><p>addresses the urgency of a patient's symptoms.</p><p>Explanation of Options:</p><p>Option A: Suggesting articles might be suitable for less urgent inquiries but is inappropriate for</p><p>symptoms that could indicate a serious condition.</p><p>Option B: Given the description of severe symptoms like headaches and dizziness, directing the</p><p>patient to emergency services is prudent. This aligns with medical guidelines that recommend</p><p>immediate professional attention for such severe symptoms.</p><p>Option C: Offering well-wishes does not address the potential seriousness of the symptoms and lacks</p><p>appropriate action.</p><p>Option D: While gathering more information is part of a detailed assessment, the immediate need</p><p>here suggests a more urgent response.</p><p>Given the potential severity of the described symptoms, Option B is the most appropriate, ensuring</p><p>the chatbot directs patients to seek urgent care when needed, potentially saving lives.</p><p>4.A Generative AI Engineer is designing a RAG application for answering user questions on technical</p><p>regulations as they learn a new sport.</p><p>What are the steps needed to build this RAG application and deploy it?</p><p>A. Ingest documents from a source C> Index the documents and saves to Vector Search C> User</p><p>submits queries against an LLM C> LLM retrieves relevant documents C> Evaluate model C> LLM</p><p>generates a response C> Deploy it using Model Serving</p><p>B. Ingest documents from a source C> Index the documents and save to Vector Search C> User</p><p>submits queries against an LLM C> LLM retrieves relevant documents C> LLM generates a response</p><p>-> Evaluate model C> Deploy it using Model Serving</p><p>C. Ingest documents from a source C> Index the documents and save to Vector Search C> Evaluate</p><p>model C> Deploy it using Model Serving</p><p>D. User submits queries against an LLM C> Ingest documents from a source C> Index the</p><p>documents and save to Vector Search C> LLM retrieves relevant documents C> LLM generates a</p><p>response C> Evaluate model C> Deploy it using Model Serving</p><p>Answer: B</p><p>Explanation:</p><p>5 / 8</p><p>https://www.dumpsinfo.com/</p><p>The Generative AI Engineer needs to follow a methodical pipeline to build and deploy a Retrieval-</p><p>Augmented Generation (RAG) application. The steps outlined in option B accurately reflect this</p><p>process:</p><p>Ingest documents from a source: This is the first step, where the engineer collects documents (e.g.,</p><p>technical regulations) that will be used for retrieval when the application answers user questions.</p><p>Index the documents and save to Vector Search: Once the documents are ingested, they need to be</p><p>embedded using a technique like embeddings (e.g., with a pre-trained model like BERT) and stored in</p><p>a vector database (such as Pinecone or FAISS). This enables fast retrieval based on user queries.</p><p>User submits queries against an LLM: Users interact with the application by submitting their queries.</p><p>These queries will be passed to the LLM.</p><p>LLM retrieves relevant documents: The LLM works with the vector store to retrieve the most relevant</p><p>documents based on their vector representations.</p><p>LLM generates a response: Using the retrieved documents, the LLM generates a response that is</p><p>tailored to the user's question.</p><p>Evaluate model: After generating responses, the system must be evaluated to ensure the retrieved</p><p>documents are relevant and the generated response is accurate. Metrics such as accuracy,</p><p>relevance, and user satisfaction can be used for evaluation.</p><p>Deploy it using Model Serving: Once the RAG pipeline is ready and evaluated, it is deployed using a</p><p>model-serving platform such as Databricks Model Serving. This enables real-time inference and</p><p>response generation for users.</p><p>By following these steps, the Generative AI Engineer ensures that the RAG application is both</p><p>efficient and effective for the task of answering technical regulation questions.</p><p>5.A Generative AI Engineer is building a RAG application that will rely on context retrieved from</p><p>source</p><p>documents that are currently in PDF format. These PDFs can contain both text and images. They</p><p>want to develop a solution using the least amount of lines of code.</p><p>Which Python package should be used to extract the text from the source documents?</p><p>A. flask</p><p>B. beautifulsoup</p><p>C. unstructured</p><p>D. numpy</p><p>Answer: C</p><p>Explanation:</p><p>Problem Context: The engineer needs to extract text from PDF documents, which may contain both</p><p>text and images. The goal is to find a Python package that simplifies this task using the least amount</p><p>of code.</p><p>Explanation of Options:</p><p>Option A: flask: Flask is a web framework for Python, not suitable for processing or extracting content</p><p>from PDFs.</p><p>Option B: beautifulsoup: Beautiful Soup is designed for parsing HTML and XML documents, not</p><p>PDFs.</p><p>Option C: unstructured: This Python package is specifically designed to work with unstructured data,</p><p>including extracting text from PDFs. It provides functionalities to handle various types of content in</p><p>documents with minimal coding, making it ideal for the task.</p><p>Option D: numpy: Numpy is a powerful library for numerical computing in Python and does not</p><p>provide any tools for text extraction from PDFs.</p><p>Given the requirement, Option C (unstructured) is the most appropriate as it directly addresses the</p><p>need to efficiently extract text from PDF documents with minimal code.</p><p>6 / 8</p><p>https://www.dumpsinfo.com/</p><p>6.A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users</p><p>on its platform while its users play online video games.</p><p>Which metric would help them increase user engagement and retention for their platform?</p><p>A. Randomness</p><p>B. Diversity of responses</p><p>C. Lack of relevance</p><p>D. Repetition of responses</p><p>Answer: B</p><p>Explanation:</p><p>In the context of designing a chatbot to engage users on a gaming platform, diversity of responses</p><p>(option B) is a key metric to increase user engagement and retention. Here’s why:</p><p>Diverse and Engaging Interactions:</p><p>A chatbot that provides varied and interesting responses will keep users engaged, especially in an</p><p>interactive environment like a gaming platform. Gamers typically enjoy dynamic and evolving</p><p>conversations, and diversity of responses helps prevent monotony, encouraging users to interact</p><p>more frequently with the bot.</p><p>Increasing Retention:</p><p>By offering different types of responses to similar queries, the chatbot can create a sense of novelty</p><p>and excitement, which enhances the user’s experience and makes them more likely to return to the</p><p>platform.</p><p>Why Other Options Are Less Effective:</p><p>A (Randomness): Random responses can be confusing or irrelevant, leading to frustration and</p><p>reducing engagement.</p><p>C (Lack of Relevance): If responses are not relevant to the user’s queries, this will degrade the user</p><p>experience and lead to disengagement.</p><p>D (Repetition of Responses): Repetitive responses can quickly bore users, making the chatbot feel</p><p>uninteresting and reducing the likelihood of continued interaction.</p><p>Thus, diversity of responses (option B) is the most effective way to keep users engaged and retain</p><p>them on the platform.</p><p>7.A Generative Al Engineer is creating an LLM system that will retrieve news articles from the year</p><p>1918 and related to a user's query and summarize them. The engineer has noticed that the</p><p>summaries are generated well but often also include an explanation of how the summary was</p><p>generated, which is undesirable.</p><p>Which change could the Generative Al Engineer perform to mitigate this issue?</p><p>A. Split the LLM output by newline characters to truncate away the summarization explanation.</p><p>B. Tune the chunk size of news articles or experiment with different embedding models.</p><p>C. Revisit their document ingestion logic, ensuring that the news articles are being ingested properly.</p><p>D. Provide few shot examples of desired output format to the system and/or user prompt.</p><p>Answer: D</p><p>Explanation:</p><p>To mitigate the issue of the LLM including explanations of how summaries are generated in its output,</p><p>the best approach is to adjust the training or prompt structure.</p><p>Here’s why Option D is effective:</p><p>Few-shot Learning: By providing specific examples of how the desired output should look (i.e., just</p><p>the summary without explanation), the model learns the preferred format. This few-shot learning</p><p>approach helps the model understand not only what content to generate but also how to format its</p><p>responses.</p><p>Prompt Engineering: Adjusting the user prompt to specify the desired output format clearly can guide</p><p>the LLM to produce summaries without additional explanatory text. Effective prompt design is crucial</p><p>in controlling the behavior of generative models.</p><p>7 / 8</p><p>https://www.dumpsinfo.com/</p><p>Why Other Options Are Less Suitable:</p><p>A: While technically feasible, splitting the output by newline and truncating could lead to loss of</p><p>important content or create awkward breaks in the summary.</p><p>B: Tuning chunk sizes or changing embedding models does not directly address the issue of the</p><p>model's tendency to generate explanations along with summaries.</p><p>C: Revisiting document ingestion logic ensures accurate source data but does not influence how the</p><p>model formats its output.</p><p>By using few-shot examples and refining the prompt, the engineer directly influences the output</p><p>format, making this approach the most targeted and effective solution.</p><p>Powered by TCPDF (www.tcpdf.org)</p><p>8 / 8</p><p>https://www.dumpsinfo.com/</p><p>http://www.tcpdf.org</p>