Question # 1
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.
Question # 2
A Generative AI Engineer received the following business requirements for an external chatbot.
The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event. What is an ideal workflow for such a chatbot?
| A. The chatbot should only look at previous event information
| B. There should be two different chatbots handling different types of user queries.
| C. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it’s an upcoming event question, send the query to a text-to-SQL model. If it’s about ticket purchasing, the customer should be redirected to a payment platform.
| D. The chatbot should only process payments
|
C. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it’s an upcoming event question, send the query to a text-to-SQL model. If it’s about ticket purchasing, the customer should be redirected to a payment platform.
Explanation:
Problem Context: The chatbot must handle various types of queries and intelligently route them to the appropriate responses or systems.
Explanation of Options:
Option A: Limiting the chatbot to only previous event information restricts its utility and does not meet the broader business requirements.
Option B: Having two separate chatbots could unnecessarily complicate user interaction and increase maintenance overhead.
Option C: Implementing a multi-step workflow where the chatbot first identifies the type of question and then routes it accordingly is the most efficient and scalable solution. This approach allows the chatbot to handle a variety of queries dynamically, improving user experience and operational efficiency.
Option D: Focusing solely on payments would not satisfy all the specified user interaction needs, such as inquiring about event details.
Option Coffers a comprehensive workflow that maximizes the chatbot’s utility and responsiveness to different user needs, aligning perfectly with the business requirements.
Question # 3
A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code. Which Python package should be used to extract the text from the source documents?
| A. flask
| B. beautifulsoup
| C. unstructured
| D. numpy
|
C. unstructured
Explanation:
Problem Context: The engineer needs to extract text from PDF documents, which may contain both text and images. The goal is to find a Python package that simplifies this task using the least amount of code.
Explanation of Options:
Option A: flask: Flask is a web framework for Python, not suitable for processing or extracting content from PDFs.
Option B: beautifulsoup: Beautiful Soup is designed for parsing HTML and XML documents, not PDFs.
Option C: unstructured: This Python package is specifically designed to work with unstructured data, including extracting text from PDFs. It provides functionalities to handle various types of content in documents with minimal coding, making it ideal for the task.
Option D: numpy: Numpy is a powerful library for numerical computing in Python and does not provide any tools for text extraction from PDFs.
Given the requirement,Option C(unstructured) is the most appropriate as it directly addresses the need to efficiently extract text from PDF documents with minimal code.
Question # 4
| A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application. What strategy should the Generative AI Engineer use? | A. Switch to using External Models instead
| B. Deploy the model using pay-per-token throughput as it comes with cost guarantees
| C. Change to a model with a fewer number of parameters in order to reduce hardware constraint issues
| D. Throttle the incoming batch of requests manually to avoid rate limiting issues
|
B. Deploy the model using pay-per-token throughput as it comes with cost guarantees
Explanation:
Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.
Explanation of Options:
Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.
Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.
Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.
Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.
OptionBis ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.
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 Al Engineer is tasked with improving the RAG quality by addressing its inflammatory outputs. Which action would be most effective in mitigating the problem of offensive text outputs?
| | A. Increase the frequency of upstream data updates | | B. Inform the user of the expected RAG behavior | | C. Restrict access to the data sources to a limited number of users | | D. Curate upstream data properly that includes manual review before it is fed into the RAG system |
D. Curate upstream data properly that includes manual review before it is fed into the RAG system
Explanation:
Addressing offensive or inflammatory outputs in a Retrieval-Augmented Generation (RAG) system is critical for improving user experience and ensuring ethical AI deployment. Here's whyDis the most effective approach:
Manual data curation: The root cause of offensive outputs often comes from the underlying data used to train the model or populate the retrieval system. By manually curating the upstream data and conducting thorough reviews before the data is fed into the RAG system, the engineer can filter out harmful, offensive, or inappropriate content.
Improving data quality: Curating data ensures the system retrieves and generates responses from a high-quality, well-vetted dataset. This directly impacts the relevance and appropriateness of the outputs from the RAG system, preventing inflammatory content from being included in responses.
Effectiveness: This strategy directly tackles the problem at its source (the data) rather than just mitigating the consequences (such as informing users or restricting access). It ensures that the system consistently provides non-offensive, relevant information.
Other options, such as increasing the frequency of data updates or informing users about behavior expectations, may not directly mitigate the generation of inflammatory outputs.
Question # 7
| What is the most suitable library for building a multi-step LLM-based workflow?
| | A. Pandas | | B. TensorFlow | | C. PySpark | | D. LangChain |
D. LangChain
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