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Databricks-Generative-AI-Engineer-Associate합격보장가능덤프 - Databricks-Generative-AI-Engineer-Associate시험대비최신덤프공부

Fast2test의 Databricks Databricks-Generative-AI-Engineer-Associate덤프로Databricks Databricks-Generative-AI-Engineer-Associate시험준비를 하면 시험패스는 간단한 일이라는걸 알게 될것입니다. Databricks Databricks-Generative-AI-Engineer-Associate덤프는 최근Databricks Databricks-Generative-AI-Engineer-Associate시험의 기출문제모음으로 되어있기에 적중율이 높습니다.시험에서 떨어지면 덤프비용 전액 환불해드리기에 우려없이 덤프를 주문하셔도 됩니다.

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

주제
소개

주제 1

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.

주제 2

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 3

Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.

>> Databricks-Generative-AI-Engineer-Associate합격보장 가능 덤프 <<

Databricks Databricks-Generative-AI-Engineer-Associate시험대비 최신 덤프공부 & Databricks-Generative-AI-Engineer-Associate시험유효자료

Fast2test 의 Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프는 PDF버전과 소프트웨어버전 두가지 버전으로 되어있는데 소프트웨어버전은 시뮬레이션버전입니다. 소프트웨어버전의 문제를 푸는 과정은 시험현장을 연상케하여 시험환경에 먼저 적응하여 실제시험에서 높은 점수를 받도록 도와드릴수 있습니다.

최신 Generative AI Engineer Databricks-Generative-AI-Engineer-Associate 무료샘플문제 (Q19-Q24):

질문 # 19
A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results.
How should they configure the endpoint to pass the secrets and credentials?

A. Pass the secrets in plain text
B. Use spark.conf.set ()
C. Add credentials using environment variables
D. Pass variables using the Databricks Feature Store API

정답：C

설명：
Context: Deploying an application that uses an MLflow Pyfunc model involves managing sensitive information such as secrets and credentials securely.
Explanation of Options:
* Option A: Use spark.conf.set(): While this method can pass configurations within Spark jobs, using it for secrets is not recommended because it may expose them in logs or Spark UI.
* Option B: Pass variables using the Databricks Feature Store API: The Feature Store API is designed for managing features for machine learning, not for handling secrets or credentials.
* Option C: Add credentials using environment variables: This is a common practice for managing credentials in a secure manner, as environment variables can be accessed securely by applications without exposing them in the codebase.
* Option D: Pass the secrets in plain text: This is highly insecure and not recommended, as it exposes sensitive information directly in the code.
Therefore,Option Cis the best method for securely passing secrets and credentials to an application, protecting them from exposure.

질문 # 20
A Generative Al Engineer is using an LLM to classify species of edible mushrooms based on text descriptions of certain features. The model is returning accurate responses in testing and the Generative Al Engineer is confident they have the correct list of possible labels, but the output frequently contains additional reasoning in the answer when the Generative Al Engineer only wants to return the label with no additional text.
Which action should they take to elicit the desired behavior from this LLM?

A. Use zero shot chain-of-thought prompting to prevent a verbose output format
B. Use few snot prompting to instruct the model on expected output format
C. Use zero shot prompting to instruct the model on expected output format
D. Use a system prompt to instruct the model to be succinct in its answer

정답：D

설명：
The LLM classifies mushroom species accurately but includes unwanted reasoning text, and the engineer wants only the label. Let's assess how to control output format effectively.
* Option A: Use few shot prompting to instruct the model on expected output format
* Few-shot prompting provides examples (e.g., input: description, output: label). It can work but requires crafting multiple examples, which is effort-intensive and less direct than a clear instruction.
* Databricks Reference:"Few-shot prompting guides LLMs via examples, effective for format control but requires careful design"("Generative AI Cookbook").
* Option B: Use zero shot prompting to instruct the model on expected output format
* Zero-shot prompting relies on a single instruction (e.g., "Return only the label") without examples. It's simpler than few-shot but may not consistently enforce succinctness if the LLM's default behavior is verbose.
* Databricks Reference:"Zero-shot prompting can specify output but may lack precision without examples"("Building LLM Applications with Databricks").
* Option C: Use zero shot chain-of-thought prompting to prevent a verbose output format
* Chain-of-Thought (CoT) encourages step-by-step reasoning, which increases verbosity-opposite to the desired outcome. This contradicts the goal of label-only output.
* Databricks Reference:"CoT prompting enhances reasoning but often results in detailed responses"("Databricks Generative AI Engineer Guide").
* Option D: Use a system prompt to instruct the model to be succinct in its answer
* A system prompt (e.g., "Respond with only the species label, no additional text") sets a global instruction for the LLM's behavior. It's direct, reusable, and effective for controlling output style across queries.
* Databricks Reference:"System prompts define LLM behavior consistently, ideal for enforcing concise outputs"("Generative AI Cookbook," 2023).
Conclusion: Option D is the most effective and straightforward action, using a system prompt to enforce succinct, label-only responses, aligning with Databricks' best practices for output control.

질문 # 21
What is an effective method to preprocess prompts using custom code before sending them to an LLM?

A. It is better not to introduce custom code to preprocess prompts as the LLM has not been trained with examples of the preprocessed prompts
B. Write a MLflow PyFunc model that has a separate function to process the prompts
C. Rather than preprocessing prompts, it's more effective to postprocess the LLM outputs to align the outputs to desired outcomes
D. Directly modify the LLM's internal architecture to include preprocessing steps

정답：B

설명：
The most effective way to preprocess prompts using custom code is to write a custom model, such as an MLflow PyFunc model. Here's a breakdown of why this is the correct approach:
* MLflow PyFunc Models:MLflow is a widely used platform for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment. APyFuncmodel is a generic Python function model that can implement custom logic, which includes preprocessing prompts.
* Preprocessing Prompts:Preprocessing could include various tasks like cleaning up the user input, formatting it according to specific rules, or augmenting it with additional context before passing it to the LLM. Writing this preprocessing as part of a PyFunc model allows the custom code to be managed, tested, and deployed easily.
* Modular and Reusable:By separating the preprocessing logic into a PyFunc model, the system becomes modular, making it easier to maintain and update without needing to modify the core LLM or retrain it.
* Why Other Options Are Less Suitable:
* A (Modify LLM's Internal Architecture): Directly modifying the LLM's architecture is highly impractical and can disrupt the model's performance. LLMs are typically treated as black-box models for tasks like prompt processing.
* B (Avoid Custom Code): While it's true that LLMs haven't been explicitly trained with preprocessed prompts, preprocessing can still improve clarity and alignment with desired input formats without confusing the model.
* C (Postprocessing Outputs): While postprocessing the output can be useful, it doesn't address the need for clean and well-formatted inputs, which directly affect the quality of the model's responses.
Thus, using an MLflow PyFunc model allows for flexible and controlled preprocessing of prompts in a scalable way, making it the most effective method.

질문 # 22
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 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.
C. 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.
D. 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.

정답：C

질문 # 23
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. numpy
B. flask
C. unstructured
D. beautifulsoup

정답：D

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 # 24
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Fast2test의Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프공부가이드에는Databricks인증 Databricks-Generative-AI-Engineer-Associate시험의 가장 최신 시험문제의 기출문제와 예상문제가 정리되어 있어Databricks인증 Databricks-Generative-AI-Engineer-Associate시험을 패스하는데 좋은 동반자로 되어드립니다. Databricks인증 Databricks-Generative-AI-Engineer-Associate시험에서 떨어지는 경우Databricks인증 Databricks-Generative-AI-Engineer-Associate덤프비용전액 환불신청을 할수 있기에 보장성이 있습니다.시험적중율이 떨어지는 경우 덤프를 빌려 공부한 것과 같기에 부담없이 덤프를 구매하셔도 됩니다.

Databricks-Generative-AI-Engineer-Associate시험대비 최신 덤프공부: https://kr.fast2test.com/Databricks-Generative-AI-Engineer-Associate-premium-file.html