최신버전Databricks-Generative-AI-Engineer-Associate유효한덤프문제시험덤프공부

IT인증자격증을 취득하는 것은 IT업계에서 자신의 경쟁율을 높이는 유력한 수단입니다. 경쟁에서 밀리지 않으려면 자격증을 많이 취득하는 편이 안전합니다.하지만 IT자격증취득은 생각보다 많이 어려운 일입니다. Databricks인증 Databricks-Generative-AI-Engineer-Associate시험은 인기자격증을 취득하는데 필요한 시험과목입니다. PassTIP는 여러분이 자격증을 취득하는 길에서의 없어서는 안될 동반자입니다. PassTIP의Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프로 자격증을 편하게 취득하는게 어떨가요?

만일Databricks Databricks-Generative-AI-Engineer-Associate인증시험을 첫 번째 시도에서 실패를 한다면 Databricks Databricks-Generative-AI-Engineer-Associate덤프비용 전액을 환불 할 것입니다. 만일 고객이 우리 제품을 구입하고 첫 번째 시도에서 성공을 하지 못 한다면 모든 정보를 확인 한 후에 구매 금액 전체를 환불 할 것 입니다. 이러한 방법으로 저희는 고객에게 어떠한 손해도 주지 않을 것을 보장합니다.

>> Databricks-Generative-AI-Engineer-Associate유효한 덤프문제 <<

최신 Generative AI Engineer Databricks-Generative-AI-Engineer-Associate 무료샘플문제 (Q54-Q59):

질문 # 54
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. Llama2-70B
C. OpenAI GPT-4
D. BGE-large

정답：D

설명：
Problem Context: The Generative AI Engineer needs a model for a Retrieval-Augmented Generation (RAG) application that provides high-quality answers, where latency and throughput are not major concerns. The key factors areconfidentialityandsensitivityof the data, as well as the requirement for all processing to be confined to internal resources without external data transmission.
Explanation of Options:
* Option A: Dolly 1.5B: This model does not typically support RAG applications as it's more focused on image generation tasks.
* Option B: OpenAI GPT-4: While GPT-4 is powerful for generating responses, its standard deployment involves cloud-based processing, which could violate the confidentiality requirements due to external data transmission.
* Option C: BGE-large: The BGE (Big Green Engine) large model is a suitable choice if it is configured to operate on-premises or within a secure internal environment that meets regulatory requirements.
Assuming this setup, BGE-large can provide high-quality answers while ensuring that data is not transmitted to third parties, thus aligning with the project's sensitivity and confidentiality needs.
* Option D: Llama2-70B: Similar to GPT-4, unless specifically set up for on-premises use, it generally relies on cloud-based services, which might risk confidential data exposure.
Given the sensitivity and confidentiality concerns,BGE-largeis assumed to be configurable for secure internal use, making it the optimal choice for this scenario.

질문 # 55
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. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.
C. 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.
D. Benchmark multiple LLMs with the same data and pick the best LLM for the job.

정답：C

설명：
* Problem Context: After receiving positive feedback for the RAG application prototype, the next step is 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 the overall performance of an RAG system.
* Option B: This option provides a systematic approach to evaluation by testing both retrieval and generation components separately. This allows for targeted improvements and a clear understanding of each component's performance, using MLflow's metrics for a structured and standardized assessment.
* Option C: Benchmarking multiple LLMs does not focus on evaluating the existing system's components but rather on comparing different models.
* Option D: Using an LLM as a judge is subjective and less reliable for systematic performance evaluation.
OptionBis the most comprehensive and structured approach, facilitating precise evaluations and improvements on specific components of the RAG system.

질문 # 56
A Generative AI Engineer has a provisioned throughput model serving endpoint as part of a RAG application and would like to monitor the serving endpoint's incoming requests and outgoing responses. The current approach is to include a micro-service in between the endpoint and the user interface to write logs to a remote server.
Which Databricks feature should they use instead which will perform the same task?

A. Vector Search
B. Lakeview
C. DBSQL
D. Inference Tables

정답：D

설명：
Problem Context: The goal is to monitor theserving endpointfor incoming requests and outgoing responses in aprovisioned throughput model serving endpointwithin aRetrieval-Augmented Generation (RAG) application. The current approach involves using a microservice to log requests and responses to a remote server, but the Generative AI Engineer is looking for a more streamlined solution within Databricks.
Explanation of Options:
* Option A: Vector Search: This feature is used to perform similarity searches within vector databases.
It doesn't provide functionality for logging or monitoring requests and responses in a serving endpoint, so it's not applicable here.
* Option B: Lakeview: Lakeview is not a feature relevant to monitoring or logging request-response cycles for serving endpoints. It might be more related to viewing data in Databricks Lakehouse but doesn't fulfill the specific monitoring requirement.
* Option C: DBSQL: Databricks SQL (DBSQL) is used for running SQL queries on data stored in Databricks, primarily for analytics purposes. It doesn't provide the direct functionality needed to monitor requests and responses in real-time for an inference endpoint.
* Option D: Inference Tables: This is the correct answer.Inference Tablesin Databricks are designed to store the results and metadata of inference runs. This allows the system to logincoming requests and outgoing responsesdirectly within Databricks, making it an ideal choice for monitoring the behavior of a provisioned serving endpoint. Inference Tables can be queried and analyzed, enabling easier monitoring and debugging compared to a custom microservice.
Thus,Inference Tablesare the optimal feature for monitoring request and response logs within the Databricks infrastructure for a model serving endpoint.

질문 # 57
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?

A. Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.
B. Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.
C. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.
D. Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.

정답：C

설명：
To design an LLM-based application that can answer employee HR questions using HR PDF documentation, the most effective approach is option D. Here's why:
* Chunking and Vector Store Embedding:HR documentation tends to be lengthy, so splitting it into smaller, manageable chunks helps optimize retrieval. These chunks are then embedded into avector store(a database that stores vector representations of text). Each chunk of text is transformed into an embeddingusing a transformer-based model, which allows for efficient similarity-based retrieval.
* Using Vector Search for Retrieval:When an employee asks a question, the system converts their query into an embedding as well. This embedding is then compared with the embeddings of the document chunks in the vector store. The most semantically similar chunks are retrieved, which ensures that the answer is based on the most relevant parts of the documentation.
* LLM to Generate a Response:Once the relevant chunks are retrieved, these chunks are passed into the LLM, which uses them as context to generate a coherent and accurate response to the employee's question.
* Why Other Options Are Less Suitable:
* A (Calculate Averaged Embeddings): Averaging embeddings might dilute important information. It doesn't provide enough granularity to focus on specific sections of documents.
* B (Summarize HR Documentation): Summarization loses the detail necessary for HR-related queries, which are often specific. It would likely miss the mark for more detailed inquiries.
* C (Interaction Matrix and ALS): This approach is better suited for recommendation systems and not for HR queries, as it's focused on collaborative filtering rather than text-based retrieval.
Thus, option D is the most effective solution for providing precise and contextual answers based on HR documentation.

질문 # 58
A Generative Al Engineer is building an LLM-based application that has an important transcription (speech-to-text) task. Speed is essential for the success of the application Which open Generative Al models should be used?

A. L!ama-2-70b-chat-hf
B. DBRX
C. MPT-30B-lnstruct
D. whisper-large-v3 (1.6B)

정답：D

설명：
The task requires an open generative AI model for a transcription (speech-to-text) task where speed is essential. Let's assess the options based on their suitability for transcription and performance characteristics, referencing Databricks' approach to model selection.
* Option A: Llama-2-70b-chat-hf
* Llama-2 is a text-based LLM optimized for chat and text generation, not speech-to-text. It lacks transcription capabilities.
* Databricks Reference:"Llama models are designed for natural language generation, not audio processing"("Databricks Model Catalog").
* Option B: MPT-30B-Instruct
* MPT-30B is another text-based LLM focused on instruction-following and text generation, not transcription. It's irrelevant for speech-to-text tasks.
* Databricks Reference: No specific mention, but MPT is categorized under text LLMs in Databricks' ecosystem, not audio models.
* Option C: DBRX
* DBRX, developed by Databricks, is a powerful text-based LLM for general-purpose generation.
It doesn't natively support speech-to-text and isn't optimized for transcription.
* Databricks Reference:"DBRX excels at text generation and reasoning tasks"("Introducing DBRX," 2023)-no mention of audio capabilities.
* Option D: whisper-large-v3 (1.6B)
* Whisper, developed by OpenAI, is an open-source model specifically designed for speech-to-text transcription. The "large-v3" variant (1.6 billion parameters) balances accuracy and efficiency, with optimizations for speed via quantization or deployment on GPUs-key for the application's requirements.
* Databricks Reference:"For audio transcription, models like Whisper are recommended for their speed and accuracy"("Generative AI Cookbook," 2023). Databricks supports Whisper integration in its MLflow or Lakehouse workflows.
Conclusion: OnlyD. whisper-large-v3is a speech-to-text model, making it the sole suitable choice. Its design prioritizes transcription, and its efficiency (e.g., via optimized inference) meets the speed requirement, aligning with Databricks' model deployment best practices.

질문 # 59
......

목표가 있다면 목표를 향해 끊임없이 달려야 멋진 인생이 됩니다. 지금의 현황에 만족하여 아무런 노력도 하지 않는다면 언젠가는 치열한 경쟁을 이겨내지 못하게 될것입니다. IT업종에 종사중이시라면 다른분들이 모두 취득하는 자격증쯤은 마련해야 되지 않겠습니까? Databricks인증 Databricks-Generative-AI-Engineer-Associate시험은 요즘 가장 인기있는 자격증 시험의 한과목입니다. IT업계에서 살아남으려면PassTIP에서Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프를 마련하여 자격증에 도전하여 자기의 자리를 찾아보세요.

Databricks-Generative-AI-Engineer-Associate시험난이도: https://www.passtip.net/Databricks-Generative-AI-Engineer-Associate-pass-exam.html

Databricks Databricks-Generative-AI-Engineer-Associate유효한 덤프문제 두 버전을 모두 구입하시면 시험에서 고득점으로 패스가능합니다, 망설이지 마십시오, PassTIP의 Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프는 가장 최신시험에 대비하여 만들어진 공부자료로서 시험패스는 한방에 끝내줍니다, Databricks Databricks-Generative-AI-Engineer-Associate유효한 덤프문제 우리는 100%시험패스를 보장하고 또 일년무료 업데이트서비스를 제공합니다, PassTIP Databricks-Generative-AI-Engineer-Associate시험난이도 는 완전히 여러분이 인증시험 준비와 안전한 시험패스를 위한 완벽한 덤프제공 사이트입니다.우리 PassTIP Databricks-Generative-AI-Engineer-Associate시험난이도의 덤프들은 응시자에 따라 ,시험 ,시험방법에 따라 알 맞춤한 퍼펙트한 자료입니다.여러분은 PassTIP Databricks-Generative-AI-Engineer-Associate시험난이도의 알맞춤 덤프들로 아주 간단하고 편하게 인증시험을 패스할 수 있습니다.많은 it인증관연 응시자들은 우리 PassTIP Databricks-Generative-AI-Engineer-Associate시험난이도가 제공하는 문제와 답으로 되어있는 덤프로 자격증을 취득하셨습니다.우리 PassTIP Databricks-Generative-AI-Engineer-Associate시험난이도 또한 업계에서 아주 좋은 이미지를 가지고 있습니다, Databricks-Generative-AI-Engineer-Associate 덤프를 공부하는 과정은 IT지식을 더 많이 배워가는 과정입니다.Databricks-Generative-AI-Engineer-Associate시험대비뿐만아니라 많은 지식을 배워드릴수 있는 덤프를 저희 사이트에서 제공해드립니다.

전하, 어서, 밤거리를 태성과 나란히 걷고 있으려니 기분이 묘하다, 두 버전을 모두 구입하시면 시험에서 고득점으로 패스가능합니다, 망설이지 마십시오, PassTIP의 Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프는 가장 최신시험에 대비하여 만들어진 공부자료로서 시험패스는 한방에 끝내줍니다.

Databricks-Generative-AI-Engineer-Associate유효한 덤프문제 시험준비에 가장 좋은 기출문제 모음 자료

우리는 100%시험패스를 보장하고 또 일년무료 업데이트서비스를 제공합니다, PassTIP 는Databricks-Generative-AI-Engineer-Associate유효한 덤프문제완전히 여러분이 인증시험 준비와 안전한 시험패스를 위한 완벽한 덤프제공 사이트입니다.우리 PassTIP의 덤프들은 응시자에 따라 ,시험 ,시험방법에 따라 알 맞춤한 퍼펙트한 자료입니다.여러분은 PassTIP의 알맞춤 덤프들로 아주 간단하고 편하게 인증시험을 패스할 수 있습니다. Databricks-Generative-AI-Engineer-Associate많은 it인증관연 응시자들은 우리 PassTIP가 제공하는 문제와 답으로 되어있는 덤프로 자격증을 취득하셨습니다.우리 PassTIP 또한 업계에서 아주 좋은 이미지를 가지고 있습니다.

Nick Lee Nick Lee

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최신 Generative AI Engineer Databricks-Generative-AI-Engineer-Associate 무료샘플문제 (Q54-Q59):

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