Keith Brooks Keith Brooks's Page de profil

Keith Brooks Keith Brooks

0 Cours inscrits • 0 Cours terminé

Biographie

Databricks Databricks-Generative-AI-Engineer-Associate인증문제 & Databricks-Generative-AI-Engineer-Associate최신업데이트인증덤프

참고: Itcertkr에서 Google Drive로 공유하는 무료, 최신 Databricks-Generative-AI-Engineer-Associate 시험 문제집이 있습니다: https://drive.google.com/open?id=1eQS0Z-d98C-s0t26TdV83GuzQ64Ju9iY

Itcertkr의 완벽한 Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프는 고객님이Databricks인증 Databricks-Generative-AI-Engineer-Associate시험을 패스하는 지름길입니다. 시간과 돈을 적게 들이는 반면 효과는 십점만점에 십점입니다. Itcertkr의 Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프를 선택하시면 고객님께서 원하시는 시험점수를 받아 자격증을 쉽게 취득할수 있습니다.

Databricks인증 Databricks-Generative-AI-Engineer-Associate시험이 너무 어려워 보여서 오르지못할 산처럼 보이시나요? 그건Itcertkr의 Databricks인증 Databricks-Generative-AI-Engineer-Associate시험문제에 대비하여 제작한Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프가 있다는 것을 모르고 있기때문입니다. Databricks인증 Databricks-Generative-AI-Engineer-Associate시험에 도전하고 싶으시다면 최강 시험패스율로 유명한Itcertkr의 Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프로 시험공부를 해보세요.시간절약은 물론이고 가격도 착해서 간단한 시험패스에 딱 좋은 선택입니다.

>> Databricks Databricks-Generative-AI-Engineer-Associate인증문제 <<

Databricks-Generative-AI-Engineer-Associate인증문제 100％시험패스 자료

Databricks Databricks-Generative-AI-Engineer-Associate시험은 Itcertkr 에서 출시한Databricks Databricks-Generative-AI-Engineer-Associate덤프로 도전하시면 됩니다. Databricks Databricks-Generative-AI-Engineer-Associate 덤프를 페펙트하게 공부하시면 시험을 한번에 패스할수 있습니다. 구매후 일년무료 업데이트 서비스를 제공해드리기에Databricks Databricks-Generative-AI-Engineer-Associate시험문제가 변경되어도 업데이트된 덤프를 받으면 가장 최신시험에 대비할수 있습니다.

최신 Generative AI Engineer Databricks-Generative-AI-Engineer-Associate 무료샘플문제 (Q26-Q31):

질문 # 26
A Generative Al Engineer needs to design an LLM pipeline to conduct multi-stage reasoning that leverages external tools. To be effective at this, the LLM will need to plan and adapt actions while performing complex reasoning tasks.
Which approach will do this?

A. Encourage the LLM to make multiple API calls in sequence without planning or structuring the calls, allowing the LLM to decide when and how to use external tools spontaneously.
B. Use a Chain-of-Thought (CoT) prompting technique to guide the LLM through a series of reasoning steps, then manually input the results from external tools for the final answer.
C. Implement a framework like ReAct which allows the LLM to generate reasoning traces and perform task-specific actions that leverage external tools if necessary.
D. Tram the LLM to generate a single, comprehensive response without interacting with any external tools, relying solely on its pre-trained knowledge.

정답：C

설명：
The task requires an LLM pipeline for multi-stage reasoning with external tools, necessitating planning, adaptability, and complex reasoning. Let's evaluate the options based on Databricks' recommendations for advanced LLM workflows.
* Option A: Train the LLM to generate a single, comprehensive response without interacting with any external tools, relying solely on its pre-trained knowledge
* This approach limits the LLM to its static knowledge base, excluding external tools and multi- stage reasoning. It can't adapt or plan actions dynamically, failing the requirements.
* Databricks Reference:"External tools enhance LLM capabilities beyond pre-trained knowledge" ("Building LLM Applications with Databricks," 2023).
* Option B: Implement a framework like ReAct which allows the LLM to generate reasoning traces and perform task-specific actions that leverage external tools if necessary
* ReAct (Reasoning + Acting) combines reasoning traces (step-by-step logic) with actions (e.g., tool calls), enabling the LLM to plan, adapt, and execute complex tasks iteratively. This meets all requirements: multi-stage reasoning, tool use, and adaptability.
* Databricks Reference:"Frameworks like ReAct enable LLMs to interleave reasoning and external tool interactions for complex problem-solving"("Generative AI Cookbook," 2023).
* Option C: Encourage the LLM to make multiple API calls in sequence without planning or structuring the calls, allowing the LLM to decide when and how to use external tools spontaneously
* Unstructured, spontaneous API calls lack planning and may lead to inefficient or incorrect tool usage. This doesn't ensure effective multi-stage reasoning or adaptability.
* Databricks Reference: Structured frameworks are preferred:"Ad-hoc tool calls can reduce reliability in complex tasks"("Building LLM-Powered Applications").
* Option D: Use a Chain-of-Thought (CoT) prompting technique to guide the LLM through a series of reasoning steps, then manually input the results from external tools for the final answer
* CoT improves reasoning but relies on manual tool interaction, breaking automation and adaptability. It's not a scalable pipeline solution.
* Databricks Reference:"Manual intervention is impractical for production LLM pipelines" ("Databricks Generative AI Engineer Guide").
Conclusion: Option B (ReAct) is the best approach, as it integrates reasoning and tool use in a structured, adaptive framework, aligning with Databricks' guidance for complex LLM workflows.

질문 # 27
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?

A. Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.
B. Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime.
C. Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.
D. 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.

정답：A

설명：
To build an LLM-powered system that accesses up-to-date news articles and stock prices, the best approach is tocreate an agentthat 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 query Delta tables (for up-to-date stock prices) via SQL and perform web searches to retrieve the latest news 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 for the latest data.
* Web Search for News: For news articles, the agent can generate search queries and retrieve the most relevant 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 when retrieving 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 address how 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 real-time nature of stock data and news updates, as it relies on pre-existing data rather than dynamic querying.
Thus, using an agent with access to both SQL for querying stock prices and web search for retrieving news articles is the best approach for ensuring up-to-date and accurate responses.

질문 # 28
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:

What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)

A. Reduce the maximum output tokens of the new model
B. Use a smaller embedding model to generate
C. Retrain the response generating model using ALiBi
D. Decrease the chunk size of embedded documents
E. Reduce the number of records retrieved from the vector database

정답：D,E

설명：
* Problem Context: After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.
* Explanation of Options:
* Option A: Use a smaller embedding model to generate- This wouldn't necessarily address the issue of prompt size exceeding the model's token limit.
* Option B: Reduce the maximum output tokens of the new model- This option affects the output length, not the size of the input being too large.
* Option C: Decrease the chunk size of embedded documents- This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model's context length limitations.
* Option D: Reduce the number of records retrieved from the vector database- By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.
* Option E: Retrain the response generating model using ALiBi- Retraining the model is contrary to the stipulation not to change the response generating model.
OptionsCandDare the most effective solutions to manage the model's shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.

질문 # 29
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. Thematch 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?

A. 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.
B. 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.
C. 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.
D. 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.

정답：B

설명：
* Problem Context: The problem involves matching team members to new projects based on two main factors:
* Availability: Ensure the team members are available during the project dates.
* Profile-Project Match: Use the employee profiles (unstructured text) to find the best match for a project's scope (also unstructured text).
The two main inputs are theemployee profilesandproject scopes, both of which are unstructured. This means traditional rule-based systems (e.g., simple keyword matching) would be inefficient, especially when working with large datasets.
* Explanation of Options: Let's break down the provided options to understand why D is the most optimal answer.
* Option Asuggests embedding project scopes into a vector store and then performing retrieval using team member profiles. While embedding project scopes into a vector store is a valid technique, it skips an important detail: the focus should primarily be on embedding employee profiles because we're matching the profiles to a new project, not the other way around.
* Option Binvolves using a large language model (LLM) to extract keywords from the project scope and perform keyword matching on employee profiles. While LLMs can help with keyword extraction, this approach is too simplistic and doesn't leverage advanced retrieval techniques like vector embeddings, which can handle the nuanced and rich semantics of unstructured data. This approach may miss out on subtle but important similarities.
* Option Csuggests calculating a similarity score between each team member's profile and project scope. While this is a good idea, it doesn't specify how to handle the unstructured nature of data efficiently. Iterating through each member's profile individually could be computationally expensive in large teams. It also lacks the mention of using a vector store or an efficient retrieval mechanism.
* Option Dis the correct approach. Here's why:
* Embedding team profiles into a vector store: Using a vector store allows for efficient similarity searches on unstructured data. Embedding the team member profiles into vectors captures their semantics in a way that is far more flexible than keyword-based matching.
* Using project scope for retrieval: Instead of matching keywords, this approach suggests using vector embeddings and similarity search algorithms (e.g., cosine similarity) to find the team members whose profiles most closely align with the project scope.
* Filtering based on availability: Once the best-matched candidates are retrieved based on profile similarity, filtering them by availability ensures that the system provides a practically useful result.
This method efficiently handles large-scale datasets by leveragingvector embeddingsandsimilarity search techniques, both of which are fundamental tools inGenerative AI engineeringfor handling unstructured text.
* Technical References:
* Vector embeddings: In this approach, the unstructured text (employee profiles and project scopes) is converted into high-dimensional vectors using pretrained models (e.g., BERT, Sentence-BERT, or custom embeddings). These embeddings capture the semantic meaning of the text, making it easier to perform similarity-based retrieval.
* Vector stores: Solutions likeFAISSorMilvusallow storing and retrieving large numbers of vector embeddings quickly. This is critical when working with large teams where querying through individual profiles sequentially would be inefficient.
* LLM Integration: Large language models can assist in generating embeddings for both employee profiles and project scopes. They can also assist in fine-tuning similarity measures, ensuring that the retrieval system captures the nuances of the text data.
* Filtering: After retrieving the most similar profiles based on the project scope, filtering based on availability ensures that only team members who are free for the project are considered.
This system is scalable, efficient, and makes use of the latest techniques inGenerative AI, such as vector embeddings and semantic search.

질문 # 30
A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window.
Which model fits this need?

A. DBRX
B. DistilBERT
C. Llama2-70B
D. MPT-30B

정답：C

설명：
* 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 its context window capabilities.
* Option C: Llama2-70B: Known for its large model size and extensive capabilities, including a large context window. It is also available as an open-source model, making it ideal for applications requiring 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 window and being available for open-source use, suitable for developing robust language understanding applications.

질문 # 31
......

IT국제공인자격증Databricks Databricks-Generative-AI-Engineer-Associate시험대비덤프를 제공하는 전문적인 사이트로서 회원님의 개인정보를 철저하게 보호해드리고 페이팔을 통한 결제라 안전한 결제를 진행할수 있습니다. Databricks Databricks-Generative-AI-Engineer-Associate 덤프외에 다른 인증시험덤프에 관심이 있으신 분은 온라인 서비스를 클릭하여 문의해주세요.

Databricks-Generative-AI-Engineer-Associate최신 업데이트 인증덤프: https://www.itcertkr.com/Databricks-Generative-AI-Engineer-Associate_exam.html

오르지 못할 산도 정복할수 있는게 Databricks-Generative-AI-Engineer-Associate덤프의 우점입니다.Databricks-Generative-AI-Engineer-Associate덤프로 시험을 패스하여 자격증을 취득하시면 굳게 닫혔던 취업문도 자신있게 두드릴수 있습니다, Itcertkr는 Databricks-Generative-AI-Engineer-Associate덤프뿐만아니라 IT인증시험에 관한 모든 덤프를 제공해드립니다, 최근 유행하는 Databricks-Generative-AI-Engineer-Associate인증시험에 도전해볼 생각은 없으신지요, Databricks Databricks-Generative-AI-Engineer-Associate 인증시험은 최근 가장 핫한 시험입니다, 만약Databricks Databricks-Generative-AI-Engineer-Associate인증시험 자격증이 있다면 일에서도 많은 변화가 있을 것입니다, 연봉상승은 물론, 자기자신만의 공간도 넓어집니다, Itcertkr Databricks-Generative-AI-Engineer-Associate최신 업데이트 인증덤프는 오래된 IT인증시험덤프를 제공해드리는 전문적인 사이트입니다.

아직 남녀 관계로 지낸 시간이 적으니 황태자 전하께서 황태자Databricks-Generative-AI-Engineer-Associate비 전하의 매력을 전부 발견하지 못했을 수도 있지요, 그때 소원은 거울에 비친 나연과 자신의 대비되는 모습에 절망했었다, 오르지 못할 산도 정복할수 있는게 Databricks-Generative-AI-Engineer-Associate덤프의 우점입니다.Databricks-Generative-AI-Engineer-Associate덤프로 시험을 패스하여 자격증을 취득하시면 굳게 닫혔던 취업문도 자신있게 두드릴수 있습니다.

최근 인기시험 Databricks-Generative-AI-Engineer-Associate인증문제 덤프문제

Itcertkr는 Databricks-Generative-AI-Engineer-Associate덤프뿐만아니라 IT인증시험에 관한 모든 덤프를 제공해드립니다, 최근 유행하는 Databricks-Generative-AI-Engineer-Associate인증시험에 도전해볼 생각은 없으신지요, Databricks Databricks-Generative-AI-Engineer-Associate 인증시험은 최근 가장 핫한 시험입니다.

만약Databricks Databricks-Generative-AI-Engineer-Associate인증시험 자격증이 있다면 일에서도 많은 변화가 있을 것입니다, 연봉상승은 물론, 자기자신만의 공간도 넓어집니다.

Keith Brooks Keith Brooks

Biographie

Edu Enloja

Liens Rapide

Ressources

Support