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Databricks-Generative-AI-Engineer-Associate勉強ガイド & Databricks-Generative-AI-Engineer-Associate日本語問題集

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近年、IT技術の急速な発展に伴って、IT技術を勉強し始める人がますます多くなっています。そこで、IT業界で働く人も多くなっています。このように、IT業界の競争が一層激しくなります。同様にIT業界で働いていて、IT夢を持っているあなたは、きっと他の人にキャッチアップされ、追い抜かれることを望まないでしょう。それでは、ずっと自分自身のスキルをアップグレードすることが必要になり、他の人に自分の強さを証明する必要があります。では、どうやって自分の能力を証明するのですか。多くの人々はIT認定試験を受験して認証資格を取ることを通して彼らの強さを証明します。あなたもIT認証資格を取りたいですか。まずDatabricksのDatabricks-Generative-AI-Engineer-Associate認定試験に合格しましょう。これはDatabricksの最も重要な試験の一つで、業界全体に認証された資格です。

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真実的なDatabricks-Generative-AI-Engineer-Associate勉強ガイド試験-試験の準備方法-一番優秀なDatabricks-Generative-AI-Engineer-Associate日本語問題集

当社DatabricksのウェブサイトTech4Examは非常に安全で定期的なプラットフォームです。第一に、Databricks-Generative-AI-Engineer-Associate試験トレントの購入プロセス中に会社のウェブサイトのセキュリティを保証します。第二に、Databricks-Generative-AI-Engineer-Associate模擬テストの購入に関するすべての顧客情報については、専門の担当者が管理し、情報開示は一切行われません。最後になりましたが、最も重要なのは、Databricks-Generative-AI-Engineer-Associate試験の教材には、98％から100％の高い合格率に基づく高品質のメリットがあります。 Databricks Certified Generative AI Engineer Associateデータは他の言葉よりも雄弁です。 Databricks-Generative-AI-Engineer-Associateトレーニング準備に自信を持ってください。

Databricks Certified Generative AI Engineer Associate 認定 Databricks-Generative-AI-Engineer-Associate 試験問題 (Q55-Q60):

質問 # 55
Which TWO chain components are required for building a basic LLM-enabled chat application that includes conversational capabilities, knowledge retrieval, and contextual memory?

A. Chat loaders
B. Vector Stores
C. External tools
D. (Q)
E. Conversation Buffer Memory
F. React Components

正解：B、E

解説：
Building a basic LLM-enabled chat application with conversational capabilities, knowledge retrieval, and contextual memory requires specific components that work together to process queries, maintain context, and retrieve relevant information. Databricks' Generative AI Engineer documentation outlines key components for such systems, particularly in the context of frameworks like LangChain or Databricks' MosaicML integrations. Let's evaluate the required components:
* Understanding the Requirements:
* Conversational capabilities: The app must generate natural, coherent responses.
* Knowledge retrieval: It must access external or domain-specific knowledge.
* Contextual memory: It must remember prior interactions in the conversation.
* Databricks Reference:"A typical LLM chat application includes a memory component to track conversation history and a retrieval mechanism to incorporate external knowledge"("Databricks Generative AI Cookbook," 2023).
* Evaluating the Options:
* A. (Q): This appears incomplete or unclear (possibly a typo). Without further context, it's not a valid component.
* B. Vector Stores: These store embeddings of documents or knowledge bases, enabling semantic search and retrieval of relevant information for the LLM. This is critical for knowledge retrieval in a chat application.
* Databricks Reference:"Vector stores, such as those integrated with Databricks' Lakehouse, enable efficient retrieval of contextual data for LLMs"("Building LLM Applications with Databricks").
* C. Conversation Buffer Memory: This component stores the conversation history, allowing the LLM to maintain context across multiple turns. It's essential for contextual memory.
* Databricks Reference:"Conversation Buffer Memory tracks prior user inputs and LLM outputs, ensuring context-aware responses"("Generative AI Engineer Guide").
* D. External tools: These (e.g., APIs or calculators) enhance functionality but aren't required for a basicchat app with the specified capabilities.
* E. Chat loaders: These might refer to data loaders for chat logs, but they're not a core chain component for conversational functionality or memory.
* F. React Components: These relate to front-end UI development, not the LLM chain's backend functionality.
* Selecting the Two Required Components:
* Forknowledge retrieval, Vector Stores (B) are necessary to fetch relevant external data, a cornerstone of Databricks' RAG-based chat systems.
* Forcontextual memory, Conversation Buffer Memory (C) is required to maintain conversation history, ensuring coherent and context-aware responses.
* While an LLM itself is implied as the core generator, the question asks for chain components beyond the model, making B and C the minimal yet sufficient pair for a basic application.
Conclusion: The two required chain components areB. Vector StoresandC. Conversation Buffer Memory, as they directly address knowledge retrieval and contextual memory, respectively, aligning with Databricks' documented best practices for LLM-enabled chat applications.

質問 # 56
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. Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.
C. Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime.
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.

正解：A

解説：
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.

質問 # 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. numpy
B. flask
C. beautifulsoup
D. unstructured

正解：D

解説：
* 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.

質問 # 58
A Generative AI Engineer is creating an agent-based LLM system for their favorite monster truck team. The system can answer text based questions about the monster truck team, lookup event dates via an API call, or query tables on the team's latest standings.
How could the Generative AI Engineer best design these capabilities into their system?

A. Write a system prompt for the agent listing available tools and bundle it into an agent system that runs a number of calls to solve a query.
B. Instruct the LLM to respond with "RAG", "API", or "TABLE" depending on the query, then use text parsing and conditional statements to resolve the query.
C. Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.
D. Build a system prompt with all possible event dates and table information in the system prompt. Use a RAG architecture to lookup generic text questions and otherwise leverage the information in the system prompt.

正解：A

解説：
In this scenario, the Generative AI Engineer needs to design a system that can handle different types of queries about the monster truck team. The queries may involve text-based information, API lookups for event dates, or table queries for standings. The best solution is to implement atool-based agent system.
Here's how option B works, and why it's the most appropriate answer:
* System Design Using Agent-Based Model:In modern agent-based LLM systems, you can design a system where the LLM (Large Language Model) acts as a central orchestrator. The model can "decide" which tools to use based on the query. These tools can include API calls, table lookups, or natural language searches. The system should contain asystem promptthat informs the LLM about the available tools.
* System Prompt Listing Tools:By creating a well-craftedsystem prompt, the LLM knows which tools are at its disposal. For instance, one tool may query an external API for event dates, another might look up standings in a database, and a third may involve searching a vector database for general text-based information. Theagentwill be responsible for calling the appropriate tool depending on the query.
* Agent Orchestration of Calls:The agent system is designed to execute a series of steps based on the incoming query. If a user asks for the next event date, the system will recognize this as a task that requires an API call. If the user asks about standings, the agent might query the appropriate table in the database. For text-based questions, it may call a search function over ingested data. The agent orchestrates this entire process, ensuring the LLM makes calls to the right resources dynamically.
* Generative AI Tools and Context:This is a standard architecture for integrating multiple functionalities into a system where each query requires different actions. The core design in option B is efficient because it keeps the system modular and dynamic by leveraging tools rather than overloading the LLM with static information in a system prompt (like option D).
* Why Other Options Are Less Suitable:
* A (RAG Architecture): While relevant, simply ingesting PDFs into a vector store only helps with text-based retrieval. It wouldn't help with API lookups or table queries.
* C (Conditional Logic with RAG/API/TABLE): Although this approach works, it relies heavily on manual text parsing and might introduce complexity when scaling the system.
* D (System Prompt with Event Dates and Standings): Hardcoding dates and table information into a system prompt isn't scalable. As the standings or events change, the system would need constant updating, making it inefficient.
By bundling multiple tools into a single agent-based system (as in option B), the Generative AI Engineer can best handle the diverse requirements of this system.

質問 # 59
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. Dolly 1.5B
B. OpenAI GPT-4
C. Llama2-70B
D. BGE-large

正解：D

解説：
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.

質問 # 60
......

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Databricks-Generative-AI-Engineer-Associate日本語問題集: https://www.tech4exam.com/Databricks-Generative-AI-Engineer-Associate-pass-shiken.html

Databricks-Generative-AI-Engineer-Associate試験ガイドのバージョンは、学習レベルと条件が異なるすべての学習者に適合するように継続的に改善されています、Databricks Databricks-Generative-AI-Engineer-Associate勉強ガイド証明書はすべてを表しているわけではありませんが、あなたの向上心や性格さえ、学ぶ能力について上司に何かを伝えることができます、クライアントにDatabricks-Generative-AI-Engineer-Associate学習準備で一流のサービスを提供します、Databricks-Generative-AI-Engineer-Associate学習教材の使用に関してご意見やご意見がありましたら、お知らせください、Databricks-Generative-AI-Engineer-Associate準備資料の内容については、専門家によって簡素化され、ディスプレイは効果的に設計されています、Databricks Databricks-Generative-AI-Engineer-Associate勉強ガイドこれは非常に価値がある試験なのですから、きっとあなたが念願を達成するのを助けられます、Databricks Databricks-Generative-AI-Engineer-Associate勉強ガイド自分を信じて、あなたは完璧にそれをすることができます！

何に云ってるんだ、ペテン野郎、強い経済はまた、伝統的な仕事を選ぶことを好むかろうじて独立した労働者が伝統的な仕事に戻ることを可能にします、Databricks-Generative-AI-Engineer-Associate試験ガイドのバージョンは、学習レベルと条件が異なるすべての学習者に適合するように継続的に改善されています。

準備するDatabricks Databricks-Generative-AI-Engineer-Associate 試験は簡単に一番いいDatabricks-Generative-AI-Engineer-Associate勉強ガイド: Databricks Certified Generative AI Engineer Associate

証明書はすべてを表しているわけではありませんが、あなたの向上心や性格さえ、学ぶ能力について上司に何かを伝えることができます、クライアントにDatabricks-Generative-AI-Engineer-Associate学習準備で一流のサービスを提供します、Databricks-Generative-AI-Engineer-Associate学習教材の使用に関してご意見やご意見がありましたら、お知らせください。

Databricks-Generative-AI-Engineer-Associate準備資料の内容については、専門家によって簡素化され、ディスプレイは効果的に設計されています。

さらに、Tech4Exam Databricks-Generative-AI-Engineer-Associateダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1Xmz_Bfa744SKVoTlDDQ7BOSQVV5AibIo

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