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NEW QUESTION # 32
A Generative Al Engineer is using an LLM to classify species of edible mushrooms based on text descriptions of certain features. The model is returning accurate responses in testing and the Generative Al Engineer is confident they have the correct list of possible labels, but the output frequently contains additional reasoning in the answer when the Generative Al Engineer only wants to return the label with no additional text.
Which action should they take to elicit the desired behavior from this LLM?
Answer: B
Explanation:
The LLM classifies mushroom species accurately but includes unwanted reasoning text, and the engineer wants only the label. Let's assess how to control output format effectively.
* Option A: Use few shot prompting to instruct the model on expected output format
* Few-shot prompting provides examples (e.g., input: description, output: label). It can work but requires crafting multiple examples, which is effort-intensive and less direct than a clear instruction.
* Databricks Reference:"Few-shot prompting guides LLMs via examples, effective for format control but requires careful design"("Generative AI Cookbook").
* Option B: Use zero shot prompting to instruct the model on expected output format
* Zero-shot prompting relies on a single instruction (e.g., "Return only the label") without examples. It's simpler than few-shot but may not consistently enforce succinctness if the LLM's default behavior is verbose.
* Databricks Reference:"Zero-shot prompting can specify output but may lack precision without examples"("Building LLM Applications with Databricks").
* Option C: Use zero shot chain-of-thought prompting to prevent a verbose output format
* Chain-of-Thought (CoT) encourages step-by-step reasoning, which increases verbosity-opposite to the desired outcome. This contradicts the goal of label-only output.
* Databricks Reference:"CoT prompting enhances reasoning but often results in detailed responses"("Databricks Generative AI Engineer Guide").
* Option D: Use a system prompt to instruct the model to be succinct in its answer
* A system prompt (e.g., "Respond with only the species label, no additional text") sets a global instruction for the LLM's behavior. It's direct, reusable, and effective for controlling output style across queries.
* Databricks Reference:"System prompts define LLM behavior consistently, ideal for enforcing concise outputs"("Generative AI Cookbook," 2023).
Conclusion: Option D is the most effective and straightforward action, using a system prompt to enforce succinct, label-only responses, aligning with Databricks' best practices for output control.
NEW QUESTION # 33
A Generative AI Engineer has been asked to build an LLM-based question-answering application. The application should take into account new documents that are frequently published. The engineer wants to build this application with the least cost and least development effort and have it operate at the lowest cost possible.
Which combination of chaining components and configuration meets these requirements?
Answer: D
Explanation:
Problem Context: The task is to build an LLM-based question-answering application that integrates new documents frequently with minimal costs and development efforts.
Explanation of Options:
* Option A: Utilizes a prompt and a retriever, with the retriever output being fed into the LLM. This setup is efficient because it dynamically updates the data pool via the retriever, allowing the LLM to provide up-to-date answers based on the latest documents without needing tofrequently retrain the model. This method offers a balance of cost-effectiveness and functionality.
* Option B: Requires frequent retraining of the LLM, which is costly and labor-intensive.
* Option C: Only involves prompt engineering and an LLM, which may not adequately handle the requirement for incorporating new documents unless it's part of an ongoing retraining or updating mechanism, which would increase costs.
* Option D: Involves an agent and a fine-tuned LLM, which could be overkill and lead to higher development and operational costs.
Option Ais the most suitable as it provides a cost-effective, minimal development approach while ensuring the application remains up-to-date with new information.
NEW QUESTION # 34
A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.
Which will fulfill their need?
Answer: B
Explanation:
When prioritizing cost and latency over quality in a Large Language Model (LLM)-based application, it is crucial to select a configuration that minimizes both computational resources and latency while still providing reasonable performance. Here's why D is the best choice:
Context length: The context length of 512 tokens aligns with the chunk size used for the documents (maximum of 512 tokens per chunk). This is sufficient for capturing the needed information and generating responses without unnecessary overhead.
Smallest model size: The model with a size of 0.13GB is significantly smaller than the other options. This small footprint ensures faster inference times and lower memory usage, which directly reduces both latency and cost.
Embedding dimension: While the embedding dimension of 384 is smaller than the other options, it is still adequate for tasks where cost and speed are more important than precision and depth of understanding.
This setup achieves the desired balance between cost-efficiency and reasonable performance in a latency-sensitive, cost-conscious application.
NEW QUESTION # 35
A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users on its platform while its users play online video games.
Which metric would help them increase user engagement and retention for their platform?
Answer: D
Explanation:
In the context of designing a chatbot to engage users on a gaming platform, diversity of responses (option B) is a key metric to increase user engagement and retention. Here's why:
Diverse and Engaging Interactions:
A chatbot that provides varied and interesting responses will keep users engaged, especially in an interactive environment like a gaming platform. Gamers typically enjoy dynamic and evolving conversations, and diversity of responses helps prevent monotony, encouraging users to interact more frequently with the bot.
Increasing Retention:
By offering different types of responses to similar queries, the chatbot can create a sense of novelty and excitement, which enhances the user's experience and makes them more likely to return to the platform.
Why Other Options Are Less Effective:
A (Randomness): Random responses can be confusing or irrelevant, leading to frustration and reducing engagement.
C (Lack of Relevance): If responses are not relevant to the user's queries, this will degrade the user experience and lead to disengagement.
D (Repetition of Responses): Repetitive responses can quickly bore users, making the chatbot feel uninteresting and reducing the likelihood of continued interaction.
Thus, diversity of responses (option B) is the most effective way to keep users engaged and retain them on the platform.
NEW QUESTION # 36
A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.
Which set of high level tasks should the Generative AI Engineer's system perform?
Answer: C
Explanation:
To design an LLM-based application that can answer employee HR questions using HR PDF documentation, the most effective approach is option D. Here's why:
Chunking and Vector Store Embedding:
HR documentation tends to be lengthy, so splitting it into smaller, manageable chunks helps optimize retrieval. These chunks are then embedded into a vector store (a database that stores vector representations of text). Each chunk of text is transformed into an embedding using a transformer-based model, which allows for efficient similarity-based retrieval.
Using Vector Search for Retrieval:
When an employee asks a question, the system converts their query into an embedding as well. This embedding is then compared with the embeddings of the document chunks in the vector store. The most semantically similar chunks are retrieved, which ensures that the answer is based on the most relevant parts of the documentation.
LLM to Generate a Response:
Once the relevant chunks are retrieved, these chunks are passed into the LLM, which uses them as context to generate a coherent and accurate response to the employee's question.
Why Other Options Are Less Suitable:
A (Calculate Averaged Embeddings): Averaging embeddings might dilute important information. It doesn't provide enough granularity to focus on specific sections of documents.
B (Summarize HR Documentation): Summarization loses the detail necessary for HR-related queries, which are often specific. It would likely miss the mark for more detailed inquiries.
C (Interaction Matrix and ALS): This approach is better suited for recommendation systems and not for HR queries, as it's focused on collaborative filtering rather than text-based retrieval.
Thus, option D is the most effective solution for providing precise and contextual answers based on HR documentation.
NEW QUESTION # 37
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