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JPNTestテスト問題集を初めて使用したときにGenerative AI Engineer Databricks-Generative-AI-Engineer-Associate試験（Databricks Certified Generative AI Engineer Associate）に合格されなかった場合は、購入料金を全額ご返金いたします。

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Generative AI Engineerの基礎準備資料問題集には、Generative AI Engineer Databricks-Generative-AI-Engineer-Associate試験を受けるために必要なすべての材料が含まれています。詳細は、正確で論理的なものを作成するために業界の経験を常に使用しているGenerative AI Engineer によって研究と構成されています。

JPNTestでDatabricks Databricks-Generative-AI-Engineer-Associate問題集をチョイスする理由

JPNTestは、1週間で完璧に認定試験を準備することができる、忙しい受験者に最適な問題集を提供しております。 Databricks-Generative-AI-Engineer-Associateの問題集は、Databricksの専門家チームがベンダーの推奨する授業要綱を深く分析して作成されました。弊社のDatabricks-Generative-AI-Engineer-Associate学習材料を一回のみ使用するだけで、Databricks認証試験に合格することができます。

Databricks-Generative-AI-Engineer-AssociateはDatabricksの重要な認証であり、あなたの専門スキルを試す認定でもあります。受験者は、試験を通じて自分の能力を証明したいと考えています。 JPNTest Databricks Certified Generative AI Engineer Associate は、Generative AI Engineerの47の問題と回答を収集して作成しました。Databricks Certified Generative AI Engineer Associateの知識ポイントをカバーし、候補者の能力を強化するように設計されています。 JPNTest Databricks-Generative-AI-Engineer-Associate受験問題集を使用すると、Databricks Certified Generative AI Engineer Associateに簡単に合格し、Databricks認定を取得して、Databricksとしてのキャリアをさらに歩むことができます。

デモをダウンロードする

Databricks Certified Generative AI Engineer Associate 認定 Databricks-Generative-AI-Engineer-Associate 試験問題:

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

2. A Generative Al Engineer is building a system which will answer questions on latest stock news articles.
Which will NOT help with ensuring the outputs are relevant to financial news?

A) Increase the compute to improve processing speed of questions to allow greater relevancy analysis C Implement a profanity filter to screen out offensive language
B) Incorporate manual reviews to correct any problematic outputs prior to sending to the users
C) Implement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.

3. 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.
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.
Here's an example: {"date": "April 16, 2024", "sender_email": "[email protected]", "order_id":
"RE987D"}

4. Which indicator should be considered to evaluate the safety of the LLM outputs when qualitatively assessing LLM responses for a translation use case?

A) The accuracy and relevance of the responses
B) The ability to generate responses in code
C) The similarity to the previous language
D) The latency of the response and the length of text generated

5. 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) OpenAI GPT-4
B) BGE-large
C) Llama2-70B
D) Dolly 1.5B

質問と回答：

質問 # 1 正解： B

質問 # 2 正解： A

質問 # 3 正解： D

質問 # 4 正解： A

質問 # 5 正解： B