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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation | 14% | - Prepare data for RAG applications - Manage data quality and chunking |
| Topic 2: Evaluation and Monitoring | 12% | - Evaluate model quality and performance - Monitor deployed applications |
| Topic 3: Design Applications | 14% | - Select appropriate models - Define functional requirements - Design and architect applications |
| Topic 4: Application Development | 30% | - Integrate Databricks tools (LangChain, etc.) - Develop LLM chains - Implement prompt engineering |
| Topic 5: Assembling and Deploying Apps | 22% | - Build and deploy RAG pipelines - Use Vector Search - Package and deploy models |
| Topic 6: Governance | 8% | - Apply safety filters and guidelines - Manage permissions and logging |
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NEW QUESTION # 19
A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?
Answer: B
Explanation:
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.
NEW QUESTION # 20
A Generative AI Engineer is building an LLM to generate article summaries in the form of a type of poem, such as a haiku, given the article content. However, the initial output from the LLM does not match the desired tone or style.
Which approach will NOT improve the LLM's response to achieve the desired response?
Answer: D
Explanation:
The task at hand is to improve the LLM's ability to generate poem-like article summaries with the desired tone and style. Using aneutralizerto normalize the tone and style of the underlying documents (option B) will not help improve the LLM's ability to generate the desired poetic style. Here's why:
* Neutralizing Underlying Documents:A neutralizer aims to reduce or standardize the tone of input data. However, this contradicts the goal, which is to generate text with aspecific tone and style(like haikus). Neutralizing the source documents will strip away the richness of the content, making it harder for the LLM to generate creative, stylistic outputs like poems.
* Why Other Options Improve Results:
* A (Explicit Instructions in the Prompt): Directly instructing the LLM to generate text in a specific tone and style helps align the output with the desired format (e.g., haikus). This is a common and effective technique in prompt engineering.
* C (Few-shot Examples): Providing examples of the desired output format helps the LLM understand the expected tone and structure, making it easier to generate similar outputs.
* D (Fine-tuning the LLM): Fine-tuning the model on a dataset that contains examples of the desired tone and style is a powerful way to improve the model's ability to generate outputs that match the target format.
Therefore, using a neutralizer (option B) isnotan effective method for achieving the goal of generating stylized poetic summaries.
NEW QUESTION # 21
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error.
Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?
Answer: C
Explanation:
To fix the error in the LangChain code provided for using a simple prompt template, the correct approach is Option C. Here's a detailed breakdown of why Option C is the right choice and how it addresses the issue:
* Proper Initialization: In Option C, the LLMChain is correctly initialized with the LLM instance specified as OpenAI(), which likely represents a language model (like GPT) from OpenAI. This is crucial as it specifies which model to use for generating responses.
* Correct Use of Classes and Methods:
* The PromptTemplate is defined with the correct format, specifying that adjective is a variable within the template. This allows dynamic insertion of values into the template when generating text.
* The prompt variable is properly linked with the PromptTemplate, and the final template string is passed correctly.
* The LLMChain correctly references the prompt and the initialized OpenAI() instance, ensuring that the template and the model are properly linked for generating output.
Why Other Options Are Incorrect:
* Option A: Misuses the parameter passing in generate method by incorrectly structuring the dictionary.
* Option B: Incorrectly uses prompt.format method which does not exist in the context of LLMChain and PromptTemplate configuration, resulting in potential errors.
* Option D: Incorrect order and setup in the initialization parameters for LLMChain, which would likely lead to a failure in recognizing the correct configuration for prompt and LLM usage.
Thus, Option C is correct because it ensures that the LangChain components are correctly set up and integrated, adhering to proper syntax and logical flow required by LangChain's architecture. This setup avoids common pitfalls such as type errors or method misuses, which are evident in other options.
NEW QUESTION # 22
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 # 23
Which indicator should be considered to evaluate the safety of the LLM outputs when qualitatively assessing LLM responses for a translation use case?
Answer: C
Explanation:
* Problem Context: When assessing the safety and effectiveness of LLM outputs in a translation use case, it is essential to ensure that the translations accurately and relevantly convey the intended message. The evaluation should focus on how well the LLM understands and processes different languages and contexts.
* Explanation of Options:
* Option A: The ability to generate responses in code- This is not relevant to translation quality or safety.
* Option B: The similarity to the previous language- While ensuring that translations preserve the original's intent is important, this doesn't directly address the overall quality or safety of the translation.
* Option C: The latency of the response and the length of text generated- These operational metrics are less critical in assessing the qualitative aspects of translation safety.
* Option D: The accuracy and relevance of the responses- This is crucial in translation to ensure that the translated content is true to the original in meaning and appropriateness. Accuracy and relevance directly impact the effectiveness and safety of translations, especially in sensitive or nuanced contexts.
Thus,Option Dis the most important indicator when evaluating the safety of LLM outputs in translation, focusing on the core aspects that determine the utility and trustworthiness of translated content.
NEW QUESTION # 24
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