Databricks-Generative-AI-Engineer-Associate日本語試験情報 & Databricks-Generative-AI-Engineer-Associate受験対策

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Databricks-Generative-AI-Engineer-Associate受験対策 & Databricks-Generative-AI-Engineer-Associate練習問題集

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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	ガバナンス: 試験を受けるジェネレーティブ AI エンジニアは、このトピックのマスキング手法、ガードレール手法、および法的ライセンス要件に関する知識を習得します。
トピック 2	評価と監視: このトピックでは、LLM の選択と主要なメトリックについて説明します。さらに、Generative AI エンジニアはモデルのパフォーマンスの評価について学習します。最後に、このトピックには推論ログと Databricks 機能の使用に関するサブトピックが含まれています。
トピック 3	データ準備: Generative AI エンジニアは、特定のドキュメント構造とモデル制約のチャンキング戦略について説明します。このトピックでは、ソースドキュメント内の不要なコンテンツのフィルター処理にも重点を置いています。最後に、Generative AI エンジニアは、提供されたソースデータと形式からドキュメントコンテンツを抽出する方法についても学習します。

Databricks Certified Generative AI Engineer Associate 認定 Databricks-Generative-AI-Engineer-Associate 試験問題 (Q23-Q28):

質問 # 23
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. Dolly 1.5B
B. BGE-large
C. Llama2-70B
D. OpenAI GPT-4

正解：B

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 # 24
A Generative AI Engineer has created a RAG application which can help employees retrieve answers from an internal knowledge base, such as Confluence pages or Google Drive. The prototype application is now working with some positive feedback from internal company testers. Now the Generative Al Engineer wants to formally evaluate the system's performance and understand where to focus their efforts to further improve the system.
How should the Generative AI Engineer evaluate the system?

A. Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.
B. Benchmark multiple LLMs with the same data and pick the best LLM for the job.
C. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.
D. Curate a dataset that can test the retrieval and generation components of the system separately. Use MLflow's built in evaluation metrics to perform the evaluation on the retrieval and generation components.

正解：D

解説：
* Problem Context: After receiving positive feedback for the RAG application prototype, the next step is to formally evaluate the system to pinpoint areas for improvement.
* Explanation of Options:
* Option A: While cosine similarity scores are useful, they primarily measure similarity rather than the overall performance of an RAG system.
* Option B: This option provides a systematic approach to evaluation by testing both retrieval and generation components separately. This allows for targeted improvements and a clear understanding of each component's performance, using MLflow's metrics for a structured and standardized assessment.
* Option C: Benchmarking multiple LLMs does not focus on evaluating the existing system's components but rather on comparing different models.
* Option D: Using an LLM as a judge is subjective and less reliable for systematic performance evaluation.
OptionBis the most comprehensive and structured approach, facilitating precise evaluations and improvements on specific components of the RAG system.

質問 # 25
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.)

A. Decrease the chunk size of embedded documents
B. Retrain the response generating model using ALiBi
C. Reduce the number of records retrieved from the vector database
D. Reduce the maximum output tokens of the new model
E. Use a smaller embedding model to generate

正解：A、C

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 # 26
A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. Thematch should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text.
How should the Generative Al Engineer architect their system?

A. Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.
B. Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.
C. Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members' profiles and perform keyword matching to find the best available team member.
D. Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.

正解：A

質問 # 27
A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?

A. Query the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.
B. Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime.
C. Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.
D. Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.

正解：D

解説：
To build an LLM-powered system that accesses up-to-date news articles and stock prices, the best approach is tocreate an agentthat has access to specific tools (option D).
* Agent with SQL and Web Search Capabilities:By using an agent-based architecture, the LLM can interact with external tools. The agent can query Delta tables (for up-to-date stock prices) via SQL and perform web searches to retrieve the latest news articles. This modular approach ensures the system can access both structured (stock prices) and unstructured (news) data sources dynamically.
* Why This Approach Works:
* SQL Queries for Stock Prices: Delta tables store stock prices, which the agent can query directly for the latest data.
* Web Search for News: For news articles, the agent can generate search queries and retrieve the most relevant and recent articles, then pass them to the LLM for processing.
* Why Other Options Are Less Suitable:
* A (Summarizing News for Stock Prices): This convoluted approach would not ensure accuracy when retrieving stock prices, which are already structured and stored in Delta tables.
* B (Stock Price Volatility Queries): While this could retrieve relevant information, it doesn't address how to obtain the most up-to-date news articles.
* C (Vector Store): Storing news articles and stock prices in a vector store might not capture the real-time nature of stock data and news updates, as it relies on pre-existing data rather than dynamic querying.
Thus, using an agent with access to both SQL for querying stock prices and web search for retrieving news articles is the best approach for ensuring up-to-date and accurate responses.

質問 # 28
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