Woo Product - Beltron CBT Practice 2024

Sid Fox Sid Fox

0 Course Enrolled • 0 Course Completed

Biography

更新するD-GAI-F-01試験勉強過去問試験-試験の準備方法-最高のD-GAI-F-01受験準備

P.S. PassTestがGoogle Driveで共有している無料かつ新しいD-GAI-F-01ダンプ：https://drive.google.com/open?id=147G6WlLg9s641nCQzah1gI-Cv_Ckbgl5

現在のネットワークの全盛期で、EMCのD-GAI-F-01の認証試験を準備するのにいろいろな方法があります。PassTestが提供した最も依頼できるトレーニングの問題と解答はあなたが気楽にEMCのD-GAI-F-01の認証試験を受かることに助けを差し上げます。PassTestにEMCのD-GAI-F-01の試験に関する問題はいくつかの種類がありますから、すべてのIT認証試験の要求を満たすことができます。

EMC D-GAI-F-01 認定試験の出題範囲：

トピック
出題範囲

トピック 1

生成 AI の概要: AI 愛好家や IT プロフェッショナルにとって、この試験のセクションでは、生成 AI の基本的な概念と原則がカバーされる可能性があります。

トピック 2

Dell の Generative AI テクノロジー: Dell のシステム管理者および AI 実装者にとって、試験のこの部分はおそらく Generative AI に関連する Dell の特定の実装とツールに重点が置かれるでしょう。

トピック 3

ユースケースとアプリケーション: ビジネスアナリストやソリューションアーキテクト向けに、このセクションでは、Dell のエコシステム内での Generative AI の実用的なアプリケーションとユースケースについて説明します。

トピック 4

倫理と責任ある AI: AI を扱うすべての専門家にとって、このセクションでは、企業環境での Generative AI の倫理的な考慮事項と責任ある使用について説明します。

トピック 5

実装とベストプラクティス: IT マネージャーとシステムインテグレーターの場合、この試験の部分では、Dell のテクノロジーを使用して生成 AI ソリューションを実装するためのベストプラクティスが取り上げられる場合があります。

>> D-GAI-F-01試験勉強過去問 <<

無料PDFD-GAI-F-01試験勉強過去問 | 素晴らしい合格率のD-GAI-F-01 Exam | 初段のD-GAI-F-01: Dell GenAI Foundations Achievement

試験に合格し、マネージャーから認定を取得する必要がある場合は、D-GAI-F-01の元の質問をお勧めします。当社の製品は、最初の試験で試験をクリアするのに役立ちます。最高品質のD-GAI-F-01元の質問と競争力のある価格を提供することをお約束します。優れたサービスを提供する100％パス製品を提供しています。 1年間の学習支援サービスと、EMC D-GAI-F-01試験問題の1年間の無料更新ダウンロードを提供しています。試験に不合格の場合は、問題集の交換と全額返金をサポートします。

EMC Dell GenAI Foundations Achievement 認定 D-GAI-F-01 試験問題 (Q58-Q63):

質問 # 58
What is the first step an organization must take towards developing an Al-based application?

A. Address ethical and legal issues.
B. Develop a data strategy.
C. Develop a business strategy.
D. Prioritize Al.

正解：B

解説：
The first step an organization must take towards developing an AI-based application is to develop a data strategy. The correct answer is option D. Here's an in-depth explanation:
Importance of Data:Data is the foundation of any AI system. Without a well-defined data strategy, AI initiatives are likely to fail because the model's performance heavily depends on the quality and quantity of data.
Components of a Data Strategy:A comprehensive data strategy includes data collection, storage, management, and ensuring data quality. It also involves establishing data governance policies to maintain data integrity and security.
Alignment with Business Goals:The data strategy should align with the organization's business goals to ensure that the AI applications developed are relevant and add value.
References:
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
Marr, B. (2017). Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things. Kogan Page Publishers.

質問 # 59
What is the primary purpose oi inferencing in the lifecycle of a Large Language Model (LLM)?

A. To feed the model a large volume of data from a wide variety of subjects
B. To customize the model for a specific task by feeding it task-specific content
C. To use the model in a production, research, or test environment
D. To randomize all the statistical weights of the neural networks

正解：C

解説：
Inferencing in the lifecycle of a Large Language Model (LLM) refers to using the model in practical applications. Here's an in-depth explanation:
Inferencing:This is the phase where the trained model is deployed to make predictions or generate outputs based on new input data. It is essentially the model's application stage.
Production Use:In production, inferencing involves using the model in live applications, such as chatbots or recommendation systems, where it interacts with real users.
Research and Testing:During research and testing, inferencing is used to evaluate the model's performance, validate its accuracy, and identify areas for improvement.
References:
LeCun, Y., Bengio, Y., & Hinton, G. (2015).Deep Learning. Nature, 521(7553), 436-444.
Chollet, F. (2017). Deep Learning with Python. Manning Publications.

質問 # 60
What is the purpose of fine-tuning in the generative Al lifecycle?

A. To feed the model a large volume of data from a wide variety of subjects
B. To randomize all the statistical weights of the neural network
C. To put text into a prompt to interact with the cloud-based Al system
D. To customize the model for a specific task by feeding it task-specific content

正解：D

解説：
Customization: Fine-tuning involves adjusting a pretrained model on a smaller dataset relevant to a specific task, enhancing its performance for that particular application.

質問 # 61
A company wants to develop a language model but has limited resources.
What is the main advantage of using pretrained LLMs in this scenario?

A. They are cheaper to develop
B. They require less data
C. They save time and resources
D. They are more accurate

正解：C

解説：
Pretrained Large Language Models (LLMs) like GPT-3 are advantageous for a company with limited resources because they have already been trained on vast amounts of data. This pretraining process involves significant computational resources over an extended period, which is often beyond the capacity of smaller companies or those with limited resources.
Advantages of using pretrained LLMs:
* Cost-Effective: Developing a language model from scratch requires substantial financial investment in computing power and data storage. Pretrained models, being readily available, eliminate these initial costs.
* Time-Saving: Training a language model can take weeks or even months. Using a pretrained model allows companies to bypass this lengthy process.
* Less Data Required: Pretrained models have been trained on diverse datasets, so they require less additional data to fine-tune for specific tasks.
* Immediate Deployment: Pretrained models can be deployed quickly for production, allowing companies to focus on application-specific improvements.
In summary, the main advantage is that pretrained LLMs save time and resources for companies, especially those with limited resources, by providing a foundation that has already learned a wide range of language patterns and knowledge. This allows for quicker deployment and cost savings, as the need for extensive data collection and computational training is significantly reduced.

質問 # 62
What is Transfer Learning in the context of Language Model (LLM) customization?

A. It is where purposefully malicious inputs are provided to the model to make the model more resistant to adversarial attacks.
B. It is where you can adjust prompts to shape the model's output without modifying its underlying weights.
C. It is a type of model training that occurs when you take a base LLM that has been trained and then train it on a different task while using all its existing base weights.
D. It is a process where the model is additionally trained on something like human feedback.

正解：C

解説：
Transfer learning is a technique in AI where a pre-trained model is adapted for a different but related task.
Here's a detailed explanation:
Transfer Learning:This involves taking a base model that has been pre-trained on a large dataset and fine-tuning it on a smaller, task-specific dataset.
Base Weights:The existing base weights from the pre-trained model are reused and adjusted slightly to fit the new task, which makes the process more efficient than training a model from scratch.
Benefits:This approach leverages the knowledge the model has already acquired, reducing the amount of data and computational resources needed for training on the new task.
References:
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018).A Survey on Deep Transfer Learning. In International Conference on Artificial Neural Networks.
Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification.
In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

質問 # 63
......

EMC D-GAI-F-01認定試験の難しさで近年にほとんどの受験生は資格認定試験に合格しなっかたと良く知られます。だから、我々社の有効な試験問題集は長年にわたりEMC D-GAI-F-01認定資格試験問題集作成に取り組んだIT専門家によって書いてます。実際の試験に表示される質問と正確な解答はあなたのEMC D-GAI-F-01認定資格試験合格を手伝ってあげます。

D-GAI-F-01受験準備: https://www.passtest.jp/EMC/D-GAI-F-01-shiken.html

さらに、PassTest D-GAI-F-01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=147G6WlLg9s641nCQzah1gI-Cv_Ckbgl5

Sid Fox Sid Fox

Biography

Quick Links

Resources

Support