Databricks Databricks-Generative-AI-Engineer-Associate Test Dumps

C.   Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.

Answer DescriptionExplanation:

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.

B.   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.

Answer DescriptionExplanation:

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.

B.   Deploy the model using pay-per-token throughput as it comes with cost guarantees

Answer DescriptionExplanation:

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.

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.

Answer DescriptionExplanation:

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.

A.   Assess the quality of the retrieved context

Answer DescriptionExplanation:

In a Retrieval-Augmented Generation (RAG) system, the key to providing relevant responses lies in the quality of the retrieved context. Here’s why option A is the most appropriate solution:

Context Relevance:The RAG model generates answers based on retrieved documents or context. If the retrieved information is about an irrelevant product, it suggests that the retrieval step is failing to select the right context. The Generative AI Engineer must first assess the quality of what is being retrieved and ensure it is pertinent to the query.

Vector Search and Embedding Similarity:RAG typically uses vector search for retrieval, where embeddings of the query are matched against embeddings of product descriptions. Assessing thesemantic similarity searchprocess ensures that the closest matches are actually relevant to the query.

Fine-tuning the Retrieval Process:By improving theretrieval quality, such as tuning the embeddings or adjusting the retrieval strategy, the system can return more accurate and relevant product information.

Why Other Options Are Less Suitable:

B (Caching FAQs): Caching can speed up responses for frequently asked questions but won’t improve the relevance of the retrieved content for less frequent or new queries.

C (Use a Different LLM): Changing the LLM only affects the generation step, not the retrieval process, which is the core issue here.

D (Different Semantic Search Algorithm): This could help, but the first step is to evaluate the current retrieval context before replacing the search algorithm.

Therefore, improving and assessing the quality of the retrieved context (option A) is the first step to fixing the issue of irrelevant product information.