최신Databricks-Generative-AI-Engineer-Associate높은통과율시험공부자료덤프샘플문제

Itexamdump의Databricks Databricks-Generative-AI-Engineer-Associate덤프는 레알시험의 모든 유형을 포함하고 있습니다.객관식은 물론 드래그앤드랍,시뮬문제등 실제시험문제의 모든 유형을 포함하고 있습니다. Databricks Databricks-Generative-AI-Engineer-Associate덤프의 문제와 답은 모두 엘리트한 인증강사 및 전문가들에 의하여 만들어져Databricks Databricks-Generative-AI-Engineer-Associate 시험응시용만이 아닌 학습자료용으로도 손색이 없는 덤프입니다.저희 착한Databricks Databricks-Generative-AI-Engineer-Associate덤프 데려가세용~!

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

주제	소개
주제 1	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
주제 2	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 3	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.
주제 4	Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.

>> Databricks-Generative-AI-Engineer-Associate높은 통과율 시험공부자료 <<

적중율 좋은 Databricks-Generative-AI-Engineer-Associate높은 통과율 시험공부자료 덤프공부자료

Databricks인증 Databricks-Generative-AI-Engineer-Associate시험을 패스하고 싶다면Itexamdump에서 출시한Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프가 필수이겠죠. Databricks인증 Databricks-Generative-AI-Engineer-Associate시험을 통과하여 원하는 자격증을 취득하시면 회사에서 자기만의 위치를 단단하게 하여 인정을 받을수 있습니다.이 점이 바로 많은 IT인사들이Databricks인증 Databricks-Generative-AI-Engineer-Associate시험에 도전하는 원인이 아닐가 싶습니다. Itexamdump에서 출시한Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프 실제시험의 거의 모든 문제를 커버하고 있어 최고의 인기와 사랑을 받고 있습니다. 어느사이트의Databricks인증 Databricks-Generative-AI-Engineer-Associate공부자료도Itexamdump제품을 대체할수 없습니다.학원등록 필요없이 다른 공부자료 필요없이 덤프에 있는 문제만 완벽하게 공부하신다면Databricks인증 Databricks-Generative-AI-Engineer-Associate시험패스가 어렵지 않고 자격증취득이 쉬워집니다.

최신 Generative AI Engineer Databricks-Generative-AI-Engineer-Associate 무료샘플문제 (Q56-Q61):

질문 # 56
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. Implement a framework like ReAct which allows the LLM to generate reasoning traces and perform task-specific actions that leverage external tools if necessary.
B. Tram the LLM to generate a single, comprehensive response without interacting with any external tools, relying solely on its pre-trained knowledge.
C. 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.
D. 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.

정답：A

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 # 57
A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to 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. beautifulsoup
B. flask
C. unstructured
D. numpy

정답：C

설명：
* Problem Context: The engineer needs to extract text from PDF documents, which may contain both text and images. The goal is to find a Python package that simplifies this task using the least amount of code.
* Explanation of Options:
* Option A: flask: Flask is a web framework for Python, not suitable for processing or extracting content from 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 in documents with minimal coding, making it ideal for the task.
* Option D: numpy: Numpy is a powerful library for numerical computing in Python and does not provide any tools for text extraction from PDFs.
Given the requirement,Option C(unstructured) is the most appropriate as it directly addresses the need to efficiently extract text from PDF documents with minimal code.

질문 # 58
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.get_index()
B. vsc.create_direct_access_index()
C. vsc.similarity_search()
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 # 59
A Generative Al Engineer is responsible for developing a chatbot to enable their company's internal HelpDesk Call Center team to more quickly find related tickets and provide resolution. While creating the GenAI application work breakdown tasks for this project, they realize they need to start planning which data sources (either Unity Catalog volume or Delta table) they could choose for this application. They have collected several candidate data sources for consideration:
call_rep_history: a Delta table with primary keys representative_id, call_id. This table is maintained to calculate representatives' call resolution from fields call_duration and call start_time.
transcript Volume: a Unity Catalog Volume of all recordings as a *.wav files, but also a text transcript as *.txt files.
call_cust_history: a Delta table with primary keys customer_id, cal1_id. This table is maintained to calculate how much internal customers use the HelpDesk to make sure that the charge back model is consistent with actual service use.
call_detail: a Delta table that includes a snapshot of all call details updated hourly. It includes root_cause and resolution fields, but those fields may be empty for calls that are still active.
maintenance_schedule - a Delta table that includes a listing of both HelpDesk application outages as well as planned upcoming maintenance downtimes.
They need sources that could add context to best identify ticket root cause and resolution.
Which TWO sources do that? (Choose two.)

A. maintenance_schedule
B. transcript Volume
C. call_cust_history
D. call_detail
E. call_rep_history

정답：B,D

설명：
In the context of developing a chatbot for a company's internal HelpDesk Call Center, the key is to select data sources that provide the most contextual and detailed information about the issues being addressed. This includes identifying the root cause and suggesting resolutions. The two most appropriate sources from the list are:
* Call Detail (Option D):
* Contents: This Delta table includes a snapshot of all call details updated hourly, featuring essential fields like root_cause and resolution.
* Relevance: The inclusion of root_cause and resolution fields makes this source particularly valuable, as it directly contains the information necessary to understand and resolve the issues discussed in the calls. Even if some records are incomplete, the data provided is crucial for a chatbot aimed at speeding up resolution identification.
* Transcript Volume (Option E):
* Contents: This Unity Catalog Volume contains recordings in .wav format and text transcripts in .txt files.
* Relevance: The text transcripts of call recordings can provide in-depth context that the chatbot can analyze to understand the nuances of each issue. The chatbot can use natural language processing techniques to extract themes, identify problems, and suggest resolutions based on previous similar interactions documented in the transcripts.
Why Other Options Are Less Suitable:
* A (Call Cust History): While it provides insights into customer interactions with the HelpDesk, it focuses more on the usage metrics rather than the content of the calls or the issues discussed.
* B (Maintenance Schedule): This data is useful for understanding when services may not be available but does not contribute directly to resolving user issues or identifying root causes.
* C (Call Rep History): Though it offers data on call durations and start times, which could help in assessing performance, it lacks direct information on the issues being resolved.
Therefore, Call Detail and Transcript Volume are the most relevant data sources for a chatbot designed to assist with identifying and resolving issues in a HelpDesk Call Center setting, as they provide direct and contextual information related to customer issues.

질문 # 60
A team wants to serve a code generation model as an assistant for their software developers. It should support multiple programming languages. Quality is the primary objective.
Which of the Databricks Foundation Model APIs, or models available in the Marketplace, would be the best fit?

A. CodeLlama-34B
B. Llama2-70b
C. BGE-large
D. MPT-7b

정답：A

설명：
For a code generation model that supports multiple programming languages and where quality is the primary objective,CodeLlama-34Bis the most suitable choice. Here's the reasoning:
* Specialization in Code Generation:CodeLlama-34B is specifically designed for code generation tasks.
This model has been trained with a focus on understanding and generating code, which makes it particularly adept at handling various programming languages and coding contexts.
* Capacity and Performance:The "34B" indicates a model size of 34 billion parameters, suggesting a high capacity for handling complex tasks and generating high-quality outputs. The large model size typically correlates with better 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 more effectively than general-purpose models. It understands coding syntax, semantics, and the nuances of 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- tuned for 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 code generation tasks at high quality.
Therefore, for a high-quality, multi-language code generation application,CodeLlama-34B(option D) is the best fit.

질문 # 61
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최신Databricks-Generative-AI-Engineer-Associate높은통과율시험공부자료덤프샘플문제

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

적중율 좋은 Databricks-Generative-AI-Engineer-Associate높은 통과율 시험공부자료 덤프공부자료

최신 Generative AI Engineer Databricks-Generative-AI-Engineer-Associate 무료샘플문제 (Q56-Q61):

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