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NEW QUESTION # 19
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LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.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?
Answer: C
Explanation:
* Problem Context: The engineer is developing an LLM-powered live sports commentary platform that needs to provide real-time updates and analyses based on the latest game scores. The critical requirement here is the capability to access and integrate real-time data efficiently with the platform for immediate analysis and reporting.
* Explanation of Options:
* Option A: DatabricksIQ: While DatabricksIQ offers integration and data processing capabilities, it is more aligned with data analytics rather than real-time feature serving, which is crucial for immediate updates necessary in a live sports commentary context.
* Option B: Foundation Model APIs: These APIs facilitate interactions with pre-trained models and could be part of the solution, but on their own, they do not provide mechanisms to access real- time game scores.
* Option C: Feature Serving: This is the correct answer as feature serving specifically refers to the real-time provision of data (features) to models for prediction. This would be essential for an LLM that generates analyses based on live game data, ensuring that the commentary is current and based on the latest events in the sport.
* Option D: AutoML: This tool automates the process of applying machine learning models to real-world problems, but it does not directly provide real-time data access, which is a critical requirement for the platform.
Thus,Option C(Feature Serving) is the most suitable tool for the platform as it directly supports the real-time data needs of an LLM-powered sports commentary system, ensuring that the analyses and updates are based on the latest available information.
NEW QUESTION # 20
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?
Answer: D
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 by including 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.
NEW QUESTION # 21
A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author's web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user' s query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.
Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)
Answer: A,C
Explanation:
To optimize a chunking strategy for a Retrieval-Augmented Generation (RAG) application, the Generative AI Engineer needs a structured approach to evaluating the chunking strategy, ensuring that the chosen configuration retrieves the most relevant information and leads to accurate and coherent LLM responses. Here
' s why C and E are the correct strategies:
Strategy C: Evaluation Metrics (Recall, NDCG)
* Define an evaluation metric : Common evaluation metrics such as recall, precision, or NDCG (Normalized Discounted Cumulative Gain) measure how well the retrieved chunks match the user ' s query and the expected response.
* Recall measures the proportion of relevant information retrieved.
* NDCG is often used when you want to account for both the relevance of retrieved chunks and the ranking or order in which they are retrieved.
* Experiment with chunking strategies : Adjusting chunking strategies based on text structure (e.g., splitting by paragraph, chapter, or a fixed number of tokens) allows the engineer to experiment with various ways of slicing the text. Some chunks may better align with the user ' s query than others.
* Evaluate performance : By using recall or NDCG, the engineer can methodically test various chunking strategies to identify which one yields the highest performance. This ensures that the chunking method provides the most relevant information when embedding and retrieving data from the vector store.
Strategy E: LLM-as-a-Judge Metric
* Use the LLM as an evaluator : After retrieving chunks, the LLM can be used to evaluate the quality of answers based on the chunks provided. This could be framed as a " judge " function, where the LLM compares how well a given chunk answers previous user queries.
* Optimize based on the LLM ' s judgment : By having the LLM assess previous answers and rate their relevance and accuracy, the engineer can collect feedback on how well different chunking configurations perform in real-world scenarios.
* This metric could be a qualitative judgment on how closely the retrieved information matches the user ' s intent.
* Tune chunking parameters : Based on the LLM ' s judgment, the engineer can adjust the chunk size or structure to better align with the LLM ' s responses, optimizing retrieval for future queries.
By combining these two approaches, the engineer ensures that the chunking strategy is systematically evaluated using both quantitative (recall/NDCG) and qualitative (LLM judgment) methods. This balanced optimization process results in improved retrieval relevance and, consequently, better response generation by the LLM.
NEW QUESTION # 22
What is an effective method to preprocess prompts using custom code before sending them to an LLM?
Answer: B
Explanation:
The most effective way to preprocess prompts using custom code is to write a custom model, such as an MLflow PyFunc model. Here's a breakdown of why this is the correct approach:
MLflow PyFunc Models:
MLflow is a widely used platform for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment. A PyFunc model is a generic Python function model that can implement custom logic, which includes preprocessing prompts.
Preprocessing Prompts:
Preprocessing could include various tasks like cleaning up the user input, formatting it according to specific rules, or augmenting it with additional context before passing it to the LLM. Writing this preprocessing as part of a PyFunc model allows the custom code to be managed, tested, and deployed easily.
Modular and Reusable:
By separating the preprocessing logic into a PyFunc model, the system becomes modular, making it easier to maintain and update without needing to modify the core LLM or retrain it.
Why Other Options Are Less Suitable:
A (Modify LLM's Internal Architecture): Directly modifying the LLM's architecture is highly impractical and can disrupt the model's performance. LLMs are typically treated as black-box models for tasks like prompt processing.
B (Avoid Custom Code): While it's true that LLMs haven't been explicitly trained with preprocessed prompts, preprocessing can still improve clarity and alignment with desired input formats without confusing the model.
C (Postprocessing Outputs): While postprocessing the output can be useful, it doesn't address the need for clean and well-formatted inputs, which directly affect the quality of the model's responses.
Thus, using an MLflow PyFunc model allows for flexible and controlled preprocessing of prompts in a scalable way, making it the most effective method.
NEW QUESTION # 23
An AI developer team wants to fine-tune an open-weight model to have exceptional performance on a code generation use case. They are trying to choose the best model to start with. They want to minimize model hosting costs and are using Hugging Face model cards and spaces to explore models. Which TWO model attributes and metrics should the team focus on to make their selection?
Answer: C,D
Explanation:
To optimize for code generation performance and hosting costs , a Generative AI engineer must look at specific metrics.
* Big Code Models Leaderboard (A): This is the industry-standard benchmark for code-specific LLMs (like StarCoder or CodeLlama). It measures performance on tasks like HumanEval and MBPP, providing a direct indicator of how well the model handles programming logic.
* Number of model parameters (B): This is the primary driver of hosting costs. Larger models (e.g.,
70B) require more GPU memory (VRAM) and more expensive compute instances (like A100s/H100s) than smaller models (e.g., 7B or 13B). To minimize costs, the team should look for the smallest model that achieves a high score on the Big Code Leaderboard.
Note: MTEB (C) is for embeddings, and Chatbot Arena (D) is for general-purpose chat, neither of which is the primary metric for specialized code generation fine-tuning.
NEW QUESTION # 24
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