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Fast2test는 여러분이Databricks 인증Databricks-Generative-AI-Engineer-Associate인증시험 패스와 추후사업에 모두 도움이 되겠습니다. Fast2test제품을 선택함으로 여러분은 시간도 절약하고 돈도 절약하는 일석이조의 득을 얻을수 있습니다. 또한 구매후 일년무료 업데이트 버전을 받을수 있는 기회를 얻을수 있습니다. Databricks 인증Databricks-Generative-AI-Engineer-Associate 인증시험패스는 아주 어렵습니다. 자기에 맞는 현명한 학습자료 선택은 성공의 지름길을 내딛는 첫발입니다. 퍼펙트한 자료만이 시험에서 성공할수 있습니다. Fast2test시험문제와 답이야 말로 퍼펙트한 자료이죠. Fast2test Databricks 인증Databricks-Generative-AI-Engineer-Associate인증시험자료는 100% 패스보장을 드립니다.

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IT업계에 종사하는 분이 점점 많아지고 있는 지금 IT인증자격증은 필수품으로 되었습니다. IT인사들의 부담을 덜어드리기 위해Fast2test는Databricks인증 Databricks-Generative-AI-Engineer-Associate인증시험에 대비한 고품질 덤프를 연구제작하였습니다. Databricks인증 Databricks-Generative-AI-Engineer-Associate시험을 준비하려면 많은 정력을 기울여야 하는데 회사의 야근에 시달리면서 시험공부까지 하려면 스트레스가 이만저만이 아니겠죠. Fast2test 덤프를 구매하시면 이제 그런 고민은 끝입니다. 덤프에 있는 내용만 공부하시면 IT인증자격증 취득은 한방에 가능합니다.

최신 Generative AI Engineer Databricks-Generative-AI-Engineer-Associate 무료샘플문제 (Q33-Q38):

질문 # 33
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. call_detail
B. call_cust_history
C. call_rep_history
D. transcript Volume
E. maintenance_schedule

정답：A,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 # 34
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_delta_sync_index()
C. vsc.similarity_search()
D. vsc.create_direct_access_index()

정답：B

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 # 35
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 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.
B. 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.
C. 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.
D. 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.

정답：D

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 # 36
A Generative AI Engineer received the following business requirements for an external chatbot.
The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event.
What is an ideal workflow for such a chatbot?

A. The chatbot should only process payments
B. There should be two different chatbots handling different types of user queries.
C. The chatbot should only look at previous event information
D. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it's an upcoming event question, send the query to a text-to-SQL model. If it's about ticket purchasing, the customer should be redirected to a payment platform.

정답：D

설명：
* Problem Context: The chatbot must handle various types of queries and intelligently route them to the appropriate responses or systems.
* Explanation of Options:
* Option A: Limiting the chatbot to only previous event information restricts its utility and does not meet the broader business requirements.
* Option B: Having two separate chatbots could unnecessarily complicate user interaction and increase maintenance overhead.
* Option C: Implementing a multi-step workflow where the chatbot first identifies the type of question and then routes it accordingly is the most efficient and scalable solution. This approach allows the chatbot to handle a variety of queries dynamically, improving user experience and operational efficiency.
* Option D: Focusing solely on payments would not satisfy all the specified user interaction needs, such as inquiring about event details.
Option Coffers a comprehensive workflow that maximizes the chatbot's utility and responsiveness to different user needs, aligning perfectly with the business requirements.

질문 # 37
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. OpenAI GPT-4
B. Dolly 1.5B
C. Llama2-70B
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 # 38
......

저희는 수많은 IT자격증시험에 도전해보려 하는 IT인사들께 편리를 가져다 드리기 위해 Databricks Databricks-Generative-AI-Engineer-Associate실제시험 출제유형에 근거하여 가장 퍼펙트한 시험공부가이드를 출시하였습니다. 많은 사이트에서 판매하고 있는 시험자료보다 출중한Fast2test의 Databricks Databricks-Generative-AI-Engineer-Associate덤프는 실제시험의 거의 모든 문제를 적중하여 고득점으로 시험에서 한방에 패스하도록 해드립니다. Databricks Databricks-Generative-AI-Engineer-Associate시험은Fast2test제품으로 간편하게 도전해보시면 후회없을 것입니다.

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어차피 잃을 것도 없으니까.현중이 건넨 손수건을 가만히 내려다보고 있던 혜리가 입술을 깨물었다, 하여 차라리 평범한 연서라면 오히려 다행이라 여길 것이다, Fast2test에서 Databricks Databricks-Generative-AI-Engineer-Associate 덤프를 다운받아 공부하시면 가장 적은 시간만 투자해도Databricks Databricks-Generative-AI-Engineer-Associate시험패스하실수 있습니다.

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Databricks Databricks-Generative-AI-Engineer-Associate적중율높은시험덤프, Databricks-Generative-AI-Engineer-Associate시험대비공부자료

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최신 Generative AI Engineer Databricks-Generative-AI-Engineer-Associate 무료샘플문제 (Q33-Q38):

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Databricks Databricks-Generative-AI-Engineer-Associate적중율높은시험덤프, Databricks-Generative-AI-Engineer-Associate시험대비공부자료

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