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Databricks Databricks-Generative-AI-Engineer-Associate높은통과율시험대비공부문제 - Databricks-Generative-AI-Engineer-Associate질문과답

Databricks Databricks-Generative-AI-Engineer-Associate인증시험패스 하는 동시에 여러분의 인생에는 획기적인 일 발생한것이죠, 사업에서의 상승세는 당연한것입니다. IT업계종사자라면 누구나 이런 자격증을 취득하고싶어하리라고 믿습니다. 많은 분들이 이렇게 좋은 인증시험은 아주 어렵다고 생각합니다. 네 많습니다. 패스할확율은 아주 낮습니다. 노력하지않고야 당연히 불가능하죠.Databricks Databricks-Generative-AI-Engineer-Associate시험은 기초지식 그리고 능숙한 전업지식이 필요요 합니다. 우리Pass4Test는 여러분들한테Databricks Databricks-Generative-AI-Engineer-Associate시험을 쉽게 빨리 패스할 수 있도록 도와주는 사이트입니다. 우리Pass4Test의Databricks Databricks-Generative-AI-Engineer-Associate시험관련자료로 여러분은 짧은시간내에 간단하게 시험을 패스할수 있습니다. 시간도 절약하고 돈도 적게 들이는 이런 제안은 여러분들한테 딱 좋은 해결책이라고 봅니다.

Databricks Databricks-Generative-AI-Engineer-Associate 시험요강:

주제
소개

주제 1

Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.

주제 2

Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal
licensing requirements in this topic.

주제 3

Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.

주제 4

Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain
similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM's response, LLM guardrails, and the best LLM based on the attributes of the application.

>> Databricks Databricks-Generative-AI-Engineer-Associate높은 통과율 시험대비 공부문제 <<

최신 업데이트버전 Databricks-Generative-AI-Engineer-Associate높은 통과율 시험대비 공부문제 덤프

Pass4Test의 Databricks인증 Databricks-Generative-AI-Engineer-Associate시험덤프자료는 여러분의 시간,돈 ,정력을 아껴드립니다. 몇개월을 거쳐 시험준비공부를 해야만 패스가능한 시험을Pass4Test의 Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프는 며칠간에도 같은 시험패스 결과를 안겨드릴수 있습니다. Databricks인증 Databricks-Generative-AI-Engineer-Associate시험을 통과하여 자격증을 취득하려면Pass4Test의 Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프로 시험준비공부를 하세요.

최신 Generative AI Engineer Databricks-Generative-AI-Engineer-Associate 무료샘플문제 (Q48-Q53):

질문 # 48
A Generative AI Engineer I using the code below to test setting up a vector store:

Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?

A. vsc.similarity_search()
B. vsc.create_direct_access_index()
C. vsc.get_index()
D. vsc.create_delta_sync_index()

정답：D

설명：
Context: The Generative AI Engineer is setting up a vector store using Databricks' VectorSearchClient. This is typically done to enable fast and efficient retrieval of vectorized data for tasks like similarity searches.
Explanation of Options:
* Option A: vsc.get_index(): This function would be used to retrieve an existing index, not create one, so it would not be the logical next step immediately after creating an endpoint.
* Option B: vsc.create_delta_sync_index(): After setting up a vector store endpoint, creating an index is necessary to start populating and organizing the data. The create_delta_sync_index() function specifically creates an index that synchronizes with a Delta table, allowing automatic updates as the data changes. This is likely the most appropriate choice if the engineer plans to use dynamic data that is updated over time.
* Option C: vsc.create_direct_access_index(): This function would create an index that directly accesses the data without synchronization. While also a valid approach, it's less likely to be the next logical step if the default setup (typically accommodating changes) is intended.
* Option D: vsc.similarity_search(): This function would be used to perform searches on an existing index; however, an index needs to be created and populated with data before any search can be conducted.
Given the typical workflow in setting up a vector store, the next step after creating an endpoint is to establish an index, particularly one that synchronizes with ongoing data updates, henceOption B.

질문 # 49
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. 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.
C. 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.
D. 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.

정답：D

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 # 50
A Generative Al Engineer would like an LLM to generate formatted JSON from emails. This will require parsing and extracting the following information: order ID, date, and sender email. Here's a sample email:

They will need to write a prompt that will extract the relevant information in JSON format with the highest level of output accuracy.
Which prompt will do that?

A. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
B. You will receive customer emails and need to extract date, sender email, and order ID. You should return the date, sender email, and order ID information in JSON format.
C. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.
D. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
Here's an example: {"date": "April 16, 2024", "sender_email": "sarah.lee925@gmail.com", "order_id":
"RE987D"}

정답：D

설명：
Problem Context: The goal is to parse emails to extract certain pieces of information and output this in a structured JSON format. Clarity and specificity in the prompt design will ensure higher accuracy in the LLM' s responses.
Explanation of Options:
* Option A: Provides a general guideline but lacks an example, which helps an LLM understand the exact format expected.
* Option B: Includes a clear instruction and a specific example of the output format. Providing an example is crucial as it helps set the pattern and format in which the information should be structured, leading to more accurate results.
* Option C: Does not specify that the output should be in JSON format, thus not meeting the requirement.
* Option D: While it correctly asks for JSON format, it lacks an example that would guide the LLM on how to structure the JSON correctly.
Therefore,Option Bis optimal as it not only specifies the required format but also illustrates it with an example, enhancing the likelihood of accurate extraction and formatting by the LLM.

질문 # 51
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?

A. Repetition of responses
B. Randomness
C. Diversity of responses
D. Lack of relevance

정답：C

설명：
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. Here's why:
* Diverse and Engaging Interactions:A chatbot that provides varied and interesting responses will keep users engaged, especially in an interactive environment like a gaming platform. Gamers typically enjoy dynamic and evolving conversations, anddiversity of responseshelps prevent monotony, encouraging users to interact more frequently with the bot.
* Increasing Retention:By offering different types of responses to similar queries, the chatbot can create a sense of novelty and excitement, which enhances the user's experience and makes them more likely to return to the platform.
* Why Other Options Are Less Effective:
* A (Randomness): Random responses can be confusing or irrelevant, leading to frustration and reducing engagement.
* C (Lack of Relevance): If responses are not relevant to the user's queries, this will degrade the user experience and lead to disengagement.
* D (Repetition of Responses): Repetitive responses can quickly bore users, making the chatbot feel uninteresting and reducing the likelihood of continued interaction.
Thus,diversity of responses(option B) is the most effective way to keep users engaged and retain them on the platform.

질문 # 52
A Generative Al Engineer has built an LLM-based system that will automatically translate user text between two languages. They now want to benchmark multiple LLM's on this task and pick the best one. They have an evaluation set with known high quality translation examples. They want to evaluate each LLM using the evaluation set with a performant metric.
Which metric should they choose for this evaluation?

A. BLEU metric
B. RECALL metric
C. ROUGE metric
D. NDCG metric

정답：A

설명：
The task is to benchmark LLMs for text translation using an evaluation set with known high-quality examples, requiring a performant metric. Let's evaluate the options.
* Option A: ROUGE metric
* ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures overlap between generated and reference texts, primarily for summarization. It's less suited for translation, where precision and word order matter more.
* Databricks Reference:"ROUGE is commonly used for summarization, not translation evaluation"("Generative AI Cookbook," 2023).
* Option B: BLEU metric
* BLEU (Bilingual Evaluation Understudy) evaluates translation quality by comparing n-gram overlap with reference translations, accounting for precision and brevity. It's widely used, performant, and appropriate for this task.
* Databricks Reference:"BLEU is a standard metric for evaluating machine translation, balancing accuracy and efficiency"("Building LLM Applications with Databricks").
* Option C: NDCG metric
* NDCG (Normalized Discounted Cumulative Gain) assesses ranking quality, not text generation.
It's irrelevant for translation evaluation.
* Databricks Reference:"NDCG is suited for ranking tasks, not generative output scoring" ("Databricks Generative AI Engineer Guide").
* Option D: RECALL metric
* Recall measures retrieved relevant items but doesn't evaluate translation quality (e.g., fluency, correctness). It's incomplete for this use case.
* Databricks Reference: No specific extract, but recall alone lacks the granularity of BLEU for text generation tasks.
Conclusion: Option B (BLEU) is the best metric for translation evaluation, offering a performant and standard approach, as endorsed by Databricks' guidance on generative tasks.

질문 # 53
......

Databricks인증 Databricks-Generative-AI-Engineer-Associate시험을 패스해서 자격증을 취득하려고 하는데 시험비며 학원비며 공부자료비며 비용이 만만치 않다구요? 제일 저렴한 가격으로 제일 효과좋은Pass4Test 의 Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프를 알고 계시는지요? Pass4Test 의 Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프는 최신 시험문제에 근거하여 만들어진 시험준비공부가이드로서 학원공부 필요없이 덤프공부만으로도 시험을 한방에 패스할수 있습니다. 덤프를 구매하신분은 철저한 구매후 서비스도 받을수 있습니다.

Databricks-Generative-AI-Engineer-Associate질문과 답: https://www.pass4test.net/Databricks-Generative-AI-Engineer-Associate.html