BONUS！！！ MogiExam Databricks-Generative-AI-Engineer-Associateダンプの一部を無料でダウンロード：https://drive.google.com/open?id=18yXnbRJdwlH9yDdA8PdnAMXWakBzNEcr
MogiExamのDatabricksのDatabricks-Generative-AI-Engineer-Associate試験トレーニング資料はほかのサイトでの資料よりもっと正確的で、もっと理解やすくて、もっと権威性が高いです。MogiExamを選ぶなら、きっと君に後悔させません。もし君はいささかな心配することがあるなら、あなたはうちの商品を購入する前に、MogiExamは無料でサンプルを提供することができます。MogiExamのDatabricksのDatabricks-Generative-AI-Engineer-Associate問題集を購入するなら、君がDatabricksのDatabricks-Generative-AI-Engineer-Associate認定試験に合格する率は１００パーセントです。
| トピック | 出題範囲 |
|---|---|
| トピック 1 |
|
| トピック 2 |
|
| トピック 3 |
|
| トピック 4 |
|
>> Databricks-Generative-AI-Engineer-Associate関連復習問題集 <<
MogiExamはDatabricksのDatabricks-Generative-AI-Engineer-Associate認定試験に便利なサービスを提供するサイトで、従来の試験によってMogiExam が今年のDatabricksのDatabricks-Generative-AI-Engineer-Associate認定試験を予測してもっとも真実に近い問題集を研究し続けます。
質問 # 75
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:
What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)
正解：C、E
解説：
* Problem Context: After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.
* Explanation of Options:
* Option A: Use a smaller embedding model to generate- This wouldn't necessarily address the issue of prompt size exceeding the model's token limit.
* Option B: Reduce the maximum output tokens of the new model- This option affects the output length, not the size of the input being too large.
* Option C: Decrease the chunk size of embedded documents- This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model's context length limitations.
* Option D: Reduce the number of records retrieved from the vector database- By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.
* Option E: Retrain the response generating model using ALiBi- Retraining the model is contrary to the stipulation not to change the response generating model.
OptionsCandDare the most effective solutions to manage the model's shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.
質問 # 76
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?
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 # 77
A company has a typical RAG-enabled, customer-facing chatbot on its website.
Select the correct sequence of components a user's questions will go through before the final output is returned. Use the diagram above for reference.
正解：B
解説：
To understand how a typical RAG-enabled customer-facing chatbot processes a user's question, let's go through the correct sequence as depicted in the diagram and explained in option A:
* Embedding Model (1):The first step involves the user's question being processed through an embedding model. This model converts the text into a vector format that numerically represents the text. This step is essential for allowing the subsequent vector search to operate effectively.
* Vector Search (2):The vectors generated by the embedding model are then used in a vector search mechanism. This search identifies the most relevant documents or previously answered questions that are stored in a vector format in a database.
* Context-Augmented Prompt (3):The information retrieved from the vector search is used to create a context-augmented prompt. This step involves enhancing the basic user query with additional relevant information gathered to ensure the generated response is as accurate and informative as possible.
* Response-Generating LLM (4):Finally, the context-augmented prompt is fed into a response- generating large language model (LLM). This LLM uses the prompt to generate a coherent and contextually appropriate answer, which is then delivered as the final output to the user.
Why Other Options Are Less Suitable:
* B, C, D: These options suggest incorrect sequences that do not align with how a RAG system typically processes queries. They misplace the role of embedding models, vector search, and response generation in an order that would not facilitate effective information retrieval and response generation.
Thus, the correct sequence isembedding model, vector search, context-augmented prompt, response- generating LLM, which is option A.
質問 # 78
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?
正解：C
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 # 79
A Generative AI Engineer I using the code below to test setting up a vector store:
Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?
正解：A
解説：
Context : The Generative AI Engineer is setting up a vector store using Databricks ' VectorSearchClient.
This is typically done to enable fast and efficient retrieval of vectorized data for tasks like similarity searches.
Explanation of Options :
* Option A: vsc.get_index() : This function would be used to retrieve an existing index, not create one, so it would not be the logical next step immediately after creating an endpoint.
* Option B: vsc.create_delta_sync_index() : After setting up a vector store endpoint, creating an index is necessary to start populating and organizing the data. The create_delta_sync_index() function specifically creates an index that synchronizes with a Delta table, allowing automatic updates as the data changes. This is likely the most appropriate choice if the engineer plans to use dynamic data that is updated over time.
* Option C: vsc.create_direct_access_index() : This function would create an index that directly accesses the data without synchronization. While also a valid approach, it ' s less likely to be the next logical step if the default setup (typically accommodating changes) is intended.
* Option D: vsc.similarity_search() : This function would be used to perform searches on an existing index; however, an index needs to be created and populated with data before any search can be conducted.
Given the typical workflow in setting up a vector store, the next step after creating an endpoint is to establish an index, particularly one that synchronizes with ongoing data updates, hence Option B .
質問 # 80
......
最近の数年間で、IT領域の継続的な発展と成長に従って、Databricks-Generative-AI-Engineer-Associate認証試験はもうDatabricks試験のマイルストーンになりました。DatabricksのDatabricks-Generative-AI-Engineer-Associate「Databricks Certified Generative AI Engineer Associate」の認証試験はあなたがIT分野のプロフェッショナルになることにヘルプを差し上げます。DatabricksのDatabricks-Generative-AI-Engineer-Associateの試験問題を提供するウェブが何百ありますが、なぜ受験生は殆どMogiExamを選んだのですか。それはMogiExamにはIT領域のエリートたちが組み立てられた団体があります。その団体はDatabricksのDatabricks-Generative-AI-Engineer-Associateの認証試験の最新の資料に専攻して、あなたが気楽にDatabricksのDatabricks-Generative-AI-Engineer-Associateの認証試験に合格するためにがんばっています。MogiExamは初めにDatabricksのDatabricks-Generative-AI-Engineer-Associateの認証試験を受けるあなたが一回で成功することを保証します。MogiExamはいつまでもあなたのそばにいて、あなたと一緒に苦楽を共にするのです。
Databricks-Generative-AI-Engineer-Associate対応資料: https://www.mogiexam.com/Databricks-Generative-AI-Engineer-Associate-exam.html
BONUS！！！ MogiExam Databricks-Generative-AI-Engineer-Associateダンプの一部を無料でダウンロード：https://drive.google.com/open?id=18yXnbRJdwlH9yDdA8PdnAMXWakBzNEcr