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Databricks-Generative-AI-Engineer-Associate資料勉強 & Databricks-Generative-AI-Engineer-Associate問題数

P.S.JpshikenがGoogle Driveで共有している無料の2026 Databricks Databricks-Generative-AI-Engineer-Associateダンプ：https://drive.google.com/open?id=1U6CXDFuhnKOsg29PVY8HPLLF-Xc1lh6A

長年のマーケティングを通じて、当社のDatabricks-Generative-AI-Engineer-Associate最新の認定ガイドは多くのお客様のサポートを獲得しています。最も明白なデータは、当社の製品が毎年徐々に増加していることであり、当社の製品開発のおかげでこのような大きな成功を達成するための大きな努力です。まず、資料の更新を研究する上で非常に良い仕事をしました。さらに、Databricks-Generative-AI-Engineer-Associateの実際のDatabricks-Generative-AI-Engineer-Associate学習ガイド教材の品質は、教師によって厳密に管理されています。だから、私たちは正しい選択だと信じています。Databricks-Generative-AI-Engineer-Associate学習教材について質問がある場合は、ご相談ください。

Databricks Databricks-Generative-AI-Engineer-Associate 認定試験の出題範囲：

トピック
出題範囲

トピック 1

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.

トピック 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.

トピック 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.

トピック 4

Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal
licensing requirements in this topic.

>> Databricks-Generative-AI-Engineer-Associate資料勉強 <<

Databricks Databricks-Generative-AI-Engineer-Associate問題数 & Databricks-Generative-AI-Engineer-Associate認定資格試験

世の中に去年の自分より今年の自分が優れていないのは立派な恥です。それで、IT人材として毎日自分を充実して、Databricks-Generative-AI-Engineer-Associate問題集を学ぶ必要があります。弊社のDatabricks-Generative-AI-Engineer-Associate問題集はあなたにこのチャンスを全面的に与えられます。あなたは自分の望ましいDatabricks Databricks-Generative-AI-Engineer-Associate問題集を選らんで、学びから更なる成長を求められます。心はもはや空しくなく、生活を美しくなります。

Databricks Certified Generative AI Engineer Associate 認定 Databricks-Generative-AI-Engineer-Associate 試験問題 (Q52-Q57):

質問 # 52
A Generative Al Engineer interfaces with an LLM with prompt/response behavior that has been trained on customer calls inquiring about product availability. The LLM is designed to output "In Stock" if the product is available or only the term "Out of Stock" if not.
Which prompt will work to allow the engineer to respond to call classification labels correctly?

A. Respond with "Out of Stock" if the customer asks for a product.
B. You will be given a customer call transcript where the customer inquires about product availability.Respond with "In Stock" if the product is available or "Out of Stock" if not.
C. Respond with "In Stock" if the customer asks for a product.
D. You will be given a customer call transcript where the customer asks about product availability. The outputs are either "In Stock" or "Out of Stock". Format the output in JSON, for example: {"call_id":
"123", "label": "In Stock"}.

正解：D

解説：
* Problem Context: The Generative AI Engineer needs a prompt that will enable an LLM trained on customer call transcripts to classify and respond correctly regarding product availability. The desired response should clearly indicate whether a product is "In Stock" or "Out of Stock," and it should be formatted in a way that is structured and easy to parse programmatically, such as JSON.
* Explanation of Options:
* Option A: Respond with "In Stock" if the customer asks for a product. This prompt is too generic and does not specify how to handle the case when a product is not available, nor does it provide a structured output format.
* Option B: This option is correctly formatted and explicit. It instructs the LLM to respond based on the availability mentioned in the customer call transcript and to format the response in JSON.
This structure allows for easy integration into systems that may need to process this information automatically, such as customer service dashboards or databases.
* Option C: Respond with "Out of Stock" if the customer asks for a product. Like option A, this prompt is also insufficient as it only covers the scenario where a product is unavailable and does not provide a structured output.
* Option D: While this prompt correctly specifies how to respond based on product availability, it lacks the structured output format, making it less suitable for systems that require formatted data for further processing.
Given the requirements for clear, programmatically usable outputs,Option Bis the optimal choice because it provides precise instructions on how to respond and includes a JSON format example for structuring the output, which is ideal for automated systems or further data handling.

質問 # 53
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. Tram the LLM to generate a single, comprehensive response without interacting with any external tools, relying solely on its pre-trained knowledge.
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. 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.
D. Implement a framework like ReAct which allows the LLM to generate reasoning traces and perform task-specific actions that leverage external tools if necessary.

正解：D

解説：
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.

質問 # 54
A Generative Al Engineer has successfully ingested unstructured documents and chunked them by document sections. They would like to store the chunks in a Vector Search index. The current format of the dataframe has two columns: (i) original document file name (ii) an array of text chunks for each document.
What is the most performant way to store this dataframe?

A. Flatten the dataframe to one chunk per row, create a unique identifier for each row, and save to a Delta table
B. Store each chunk as an independent JSON file in Unity Catalog Volume. For each JSON file, the key is the document section name and the value is the array of text chunks for that section
C. Split the data into train and test set, create a unique identifier for each document, then save to a Delta table
D. First create a unique identifier for each document, then save to a Delta table

正解：A

解説：
* Problem Context: The engineer needs an efficient way to store chunks of unstructured documents to facilitate easy retrieval and search. The current dataframe consists of document filenames and associated text chunks.
* Explanation of Options:
* Option A: Splitting into train and test sets is more relevant for model training scenarios and not directly applicable to storage for retrieval in a Vector Search index.
* Option B: Flattening the dataframe such that each row contains a single chunk with a unique identifier is the most performant for storage and retrieval. This structure aligns well with how data is indexed and queried in vector search applications, making it easier to retrieve specific chunks efficiently.
* Option C: Creating a unique identifier for each document only does not address the need to access individual chunks efficiently, which is critical in a Vector Search application.
* Option D: Storing each chunk as an independent JSON file creates unnecessary overhead and complexity in managing and querying large volumes of files.
OptionBis the most efficient and practical approach, allowing for streamlined indexing and retrieval processes in a Delta table environment, fitting the requirements of a Vector Search index.

質問 # 55
A Generative Al Engineer would like an LLM to generate formatted JSON from emails. This will require parsing and extracting the following information: order ID, date, and sender email. Here's a sample email:

They will need to write a prompt that will extract the relevant information in JSON format with the highest level of output accuracy.
Which prompt will do that?

A. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
Here's an example: {"date": "April 16, 2024", "sender_email": "sarah.lee925@gmail.com", "order_id":
"RE987D"}
B. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.
C. You will receive customer emails and need to extract date, sender email, and order ID. You should return the date, sender email, and order ID information in JSON format.
D. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.

正解：A

解説：
Problem Context: The goal is to parse emails to extract certain pieces of information and output this in a structured JSON format. Clarity and specificity in the prompt design will ensure higher accuracy in the LLM' s responses.
Explanation of Options:
* Option A: Provides a general guideline but lacks an example, which helps an LLM understand the exact format expected.
* Option B: Includes a clear instruction and a specific example of the output format. Providing an example is crucial as it helps set the pattern and format in which the information should be structured, leading to more accurate results.
* Option C: Does not specify that the output should be in JSON format, thus not meeting the requirement.
* Option D: While it correctly asks for JSON format, it lacks an example that would guide the LLM on how to structure the JSON correctly.
Therefore,Option Bis optimal as it not only specifies the required format but also illustrates it with an example, enhancing the likelihood of accurate extraction and formatting by the LLM.

質問 # 56
A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names.
Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?

A. Reduce the time that the users can interact with the LLM
B. Implement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist
C. Increase the amount of compute that powers the LLM to process input faster
D. Ask the LLM to remind the user that the input is malicious but continue the conversation with the user

正解：B

解説：
In this case, the Generative AI Engineer is developing an application to generate personalized birthday poems, but there's a need to safeguard againstmalicious user inputs. The best solution is to implement asafety filter (option A) to detect harmful or inappropriate inputs.
* Safety Filter Implementation:Safety filters are essential for screening user input and preventing inappropriate content from being processed by the LLM. These filters can scan inputs for harmful language, offensive terms, or malicious content and intervene before the prompt is passed to the LLM.
* Graceful Handling of Harmful Inputs:Once the safety filter detects harmful content, the system can provide a message to the user, such as "I'm unable to assist with this request," instead of processing or responding to malicious input. This protects the system from generating harmful content and ensures a controlled interaction environment.
* Why Other Options Are Less Suitable:
* B (Reduce Interaction Time): Reducing the interaction time won't prevent malicious inputs from being entered.
* C (Continue the Conversation): While it's possible to acknowledge malicious input, it is not safe to continue the conversation with harmful content. This could lead to legal or reputational risks.
* D (Increase Compute Power): Adding more compute doesn't address the issue of harmful content and would only speed up processing without resolving safety concerns.
Therefore, implementing asafety filterthat blocks harmful inputs is the most effective technique for safeguarding the application.

質問 # 57
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Jpshikenは、お客様に学習のためのさまざまな種類のDatabricksのDatabricks-Generative-AI-Engineer-Associate練習トレントを提供し、知識を蓄積し、試験に合格し、期待されるスコアを取得する能力を高めるための信頼できる学習プラットフォームです。 Databricks-Generative-AI-Engineer-Associateスタディガイドには、オンラインでPDF、ソフトウェア、APPの3つの異なるバージョンがあります。顧客の信頼を確立するために、購入前にダウンロードできる関連するDatabricks Certified Generative AI Engineer Associate無料デモを提供しています。 Databricks-Generative-AI-Engineer-Associate試験の質問で、Databricks-Generative-AI-Engineer-Associate試験で勝つ自信があります。

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