Question # 1
| A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.
Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores? | | A. DatabricksIQ | | B. Foundation Model APIs | | C. Feature Serving | | D. AutoML |
C. Feature Serving
Question # 2
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. Reach out to the data curators directly before you have started using the trained model to let them know.
| B. Use any available data you personally created which is completely original and you can decide what license to use.
| C. Only use data explicitly labeled with an open license and ensure the license terms are followed.
| D. 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 after you have started using the trained model to let them know.
Explanation:
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.
Question # 3
| What is the most suitable library for building a multi-step LLM-based workflow?
| | A. Pandas | | B. TensorFlow | | C. PySpark | | D. LangChain |
D. LangChain
Question # 4
| 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. OpenAI GPT-4
| | C. BGE-large
| | D. Llama2-70B |
C. BGE-large
Question # 5
| 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. Pick a smaller LLM that is domain-specific | | C. Limit the number of queries a customer can send per day | | D. Use the largest LLM possible because that gives the best performance for any general queries
|
B. Pick a smaller LLM that is domain-specific
Question # 6
| A Generative AI Engineer wants to build an LLM-based solution to help a restaurant improve its online customer experience with bookings by automatically handling common customer inquiries. The goal of the solution is to minimize escalations to human intervention and phone calls while maintaining a personalized interaction. To design the solution, the Generative AI Engineer needs to define the input data to the LLM and the task it should perform.
Which input/output pair will support their goal? | | A. Input: Online chat logs; Output: Group the chat logs by users, followed by summarizing each user’s interactions | | B. Input: Online chat logs; Output: Buttons that represent choices for booking details
| | C. Input: Customer reviews; Output: Classify review sentiment
| | D. Input: Online chat logs; Output: Cancellation options |
B. Input: Online chat logs; Output: Buttons that represent choices for booking details
Question # 7
A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI. The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies. Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?
| A. Keep all articles because the RAG application needs to understand non-company content to avoid answering questions about them.
| B. Include in the system prompt that any information it sees will be about SnoPenAI, even if no data filtering is performed.
| C. Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.
| D. Consolidate all SnoPen AI related documents into a single chunk in the vector database.
|
C. Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.
Explanation:
In a Retrieval-Augmented Generation (RAG) application built to answer questions about internal documents, especially when the dataset contains irrelevant content, it's crucial to guide the system to focus on the right information. The best way to achieve this is byincluding a clear instruction in the system prompt(option C).
System Prompt as Guidance:The system prompt is an effective way to instruct the LLM to limit its focus to SnoPen AI-related content. By clearly specifying that the model should avoid answering questions unrelated to SnoPen AI, you add an additional layer of control that helps the model stay on-topic, even if irrelevant content is present in the dataset.
Why This Approach Works:The prompt acts as a guiding principle for the model, narrowing its focus to specific domains. This prevents the model from generating answers based on irrelevant content, such as advertisements or news unrelated to SnoPen AI.
Why Other Options Are Less Suitable:
A (Keep All Articles): Retaining all content, including irrelevant materials, without any filtering makes the system prone to generating answers based on unwanted data.
B (Include in the System Prompt about SnoPen AI): This option doesn’t address irrelevant content directly, and without filtering, the model might still retrieve and use irrelevant data.
D (Consolidating Documents into a Single Chunk): Grouping documents into a single chunk makes the retrieval process less efficient and won’t help filter out irrelevant content effectively.
Therefore, instructing the system in the prompt not to answer questions unrelated to SnoPen AI (option C) is the best approach to ensure the system filters out irrelevant information.
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