最高のDatabricksのDatabricks-Generative-AI-Engineer-Associate試験テストソフトウェア

P.S.MogiExamがGoogle Driveで共有している無料の2025 Databricks Databricks-Generative-AI-Engineer-Associateダンプ：https://drive.google.com/open?id=11lbH-UuSOtOInC-3pW4upZp7U6pSgUud

時間が経つとともに、MogiExamはより多くの受験生から大好評を博します。弊社のDatabricks-Generative-AI-Engineer-Associate資料は99％の成功率を持っていますから、弊社のDatabricksのDatabricks-Generative-AI-Engineer-Associate練習問題を利用したら、最もよい結果を得ることができます。弊社のDatabricks-Generative-AI-Engineer-Associate練習問題さえ使用すれば試験の成功までもっと近くなります。

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	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
トピック 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.
トピック 3	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.
トピック 4	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.

>> Databricks-Generative-AI-Engineer-Associateトレーリングサンプル <<

素敵なDatabricks-Generative-AI-Engineer-Associateトレーリングサンプル試験-試験の準備方法-一番優秀なDatabricks-Generative-AI-Engineer-Associate日本語独学書籍

最も短い時間で自分のIT技能を増強したいけれど、質の良い学習教材がないので悩んでいますか。ご心配なく、MogiExamのDatabricksのDatabricks-Generative-AI-Engineer-Associate試験トレーニング資料を手に入れるなら、ITに関する認定試験はなんでも楽に合格できます。MogiExamの DatabricksのDatabricks-Generative-AI-Engineer-Associate試験トレーニング資料は高度に認証されたIT領域の専門家の経験と創造を含めているものです。MogiExamは君にとって、ベストな選択だといっても良いです。

Databricks Certified Generative AI Engineer Associate 認定 Databricks-Generative-AI-Engineer-Associate 試験問題 (Q50-Q55):

質問 # 50
What is an effective method to preprocess prompts using custom code before sending them to an LLM?

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

正解：A

解説：
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.

質問 # 51
A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs.
Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?

A. Limit the number of relevant documents available for the RAG application to retrieve from
B. Limit the number of queries a customer can send per day
C. Pick a smaller LLM that is domain-specific
D. Use the largest LLM possible because that gives the best performance for any general queries

正解：C

解説：
For a small, cost-conscious startup in the cancer research field, choosing a domain-specific and smaller LLM is the most effective strategy. Here's whyBis the best choice:
* Domain-specific performance: A smaller LLM that has been fine-tuned for the domain of cancer research will outperform a general-purpose LLM for specialized queries. This ensures high-quality responses without needing to rely on a large, expensive LLM.
* Cost-efficiency: Smaller models are cheaper to run, both in terms of compute resources and API usage costs. A domain-specific smaller LLM can deliver good quality responses without the need for the extensive computational power required by larger models.
* Focused knowledge: In a specialized field like cancer research, having an LLM tailored to the subject matter provides better relevance and accuracy for queries, while keeping costs low.Large, general- purpose LLMs may provide irrelevant information, leading to inefficiency and higher costs.
This approach allows the startup to balance quality, cost, and customer satisfaction effectively, making it the most suitable strategy.

質問 # 52
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. BGE-large
B. CodeLlama-34B
C. MPT-7b
D. Llama2-70b

正解：B

解説：
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.

質問 # 53
A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.
Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?

A. Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to Unity Catalog using MLflow, and start a serving endpoint
B. Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container
C. Wrap the LLM's prediction function into a Flask application and serve using Gunicorn
D. Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint

正解：D

解説：
* Problem Context: The goal is to deploy a trained LLM on Databricks in the simplest and most integrated manner.
* Explanation of Options:
* Option A: This method involves unnecessary steps like logging the model as a pickle object, which is not the most efficient path in a Databricks environment.
* Option B: Logging the model with MLflow during training and then using MLflow's API to register and start serving the model is straightforward and leverages Databricks' built-in functionalities for seamless model deployment.
* Option C: Building and running a Docker container is a complex and less integrated approach within the Databricks ecosystem.
* Option D: Using Flask and Gunicorn is a more manual approach and less integrated compared to the native capabilities of Databricks and MLflow.
OptionBprovides the most straightforward and efficient process, utilizing Databricks' ecosystem to its full advantage for deploying models.

質問 # 54
When developing an LLM application, it's crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?

A. Use any available data you personally created which is completely original and you can decide what license to use.
B. Only use data explicitly labeled with an open license and ensure the license terms are followed.
C. Reach out to the data curators directly after you have started using the trained model to let them know.
D. Reach out to the data curators directly before you have started using the trained model to let them know.

正解：C

解説：
* Problem Context: When using data to train a model, it's essential to ensure compliance with licensing to avoid legal risks. Legal issues can arise from using data without permission, especially when it comes from third-party sources.
* Explanation of Options:
* Option A: Reaching out to data curatorsbeforeusing the data is an appropriate action. This allows you to ensure you have permission or understand the licensing terms before starting to use the data in your model.
* Option B: Usingoriginal datathat you personally created is always a safe option. Since you have full ownership over the data, there are no legal risks, as you control the licensing.
* Option C: Using data that is explicitly labeled with an open license and adhering to the license terms is a correct and recommended approach. This ensures compliance with legal requirements.
* Option D: Reaching out to the data curatorsafteryou have already started using the trained model isnot appropriate. If you've already used the data without understanding its licensing terms, you may have already violated the terms of use, which could lead to legal complications. It's essential to clarify the licensing termsbeforeusing the data, not after.
Thus,Option Dis not appropriate because it could expose you to legal risks by using the data without first obtaining the proper licensing permissions.

質問 # 55
......

無料のアップデートとオンラインカスタマーサービスを提供します。これは終日回線で機能します。 Databricks-Generative-AI-Engineer-Associate学習教材は、Databricks-Generative-AI-Engineer-Associate学習教材のさまざまなバージョンを提供し、Databricks-Generative-AI-Engineer-Associate学習者は時間と労力をほとんどかけずに選択できます。 Databricks-Generative-AI-Engineer-Associate試験準備は、購入後すぐに使用できます。Databricks-Generative-AI-Engineer-Associate試験の質問は5〜10分以内に送信されます。私たちはあなたの時間をあなたが見ているように貴重な自分の時間として扱いますので、無駄なプロセスで1、2分を無駄にすることはありません。使用はご安心ください。Databricks-Generative-AI-Engineer-Associate試験に必ず合格すると信じています。

Databricks-Generative-AI-Engineer-Associate日本語独学書籍: https://www.mogiexam.com/Databricks-Generative-AI-Engineer-Associate-exam.html

P.S.MogiExamがGoogle Driveで共有している無料の2025 Databricks Databricks-Generative-AI-Engineer-Associateダンプ：https://drive.google.com/open?id=11lbH-UuSOtOInC-3pW4upZp7U6pSgUud