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Databricks-Generative-AI-Engineer-Associate Practice Braindumps, Databricks-Generative-AI-Engineer-Associate Minimum Pass Score

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Databricks Certified Generative AI Engineer Associate Sample Questions (Q59-Q64):

NEW QUESTION # 59
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?

A. Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.
B. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.
C. Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.
D. Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.

Answer: B

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 avector store(a database that stores vector representations of text). Each chunk of text is transformed into an embeddingusing 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 # 60
Which TWO chain components are required for building a basic LLM-enabled chat application that includes conversational capabilities, knowledge retrieval, and contextual memory?

A. React Components
B. Conversation Buffer Memory
C. Vector Stores
D. External tools
E. (Q)
F. Chat loaders

Answer: B,C

Explanation:
Building a basic LLM-enabled chat application with conversational capabilities, knowledge retrieval, and contextual memory requires specific components that work together to process queries, maintain context, and retrieve relevant information. Databricks' Generative AI Engineer documentation outlines key components for such systems, particularly in the context of frameworks like LangChain or Databricks' MosaicML integrations. Let's evaluate the required components:
* Understanding the Requirements:
* Conversational capabilities: The app must generate natural, coherent responses.
* Knowledge retrieval: It must access external or domain-specific knowledge.
* Contextual memory: It must remember prior interactions in the conversation.
* Databricks Reference:"A typical LLM chat application includes a memory component to track conversation history and a retrieval mechanism to incorporate external knowledge"("Databricks Generative AI Cookbook," 2023).
* Evaluating the Options:
* A. (Q): This appears incomplete or unclear (possibly a typo). Without further context, it's not a valid component.
* B. Vector Stores: These store embeddings of documents or knowledge bases, enabling semantic search and retrieval of relevant information for the LLM. This is critical for knowledge retrieval in a chat application.
* Databricks Reference:"Vector stores, such as those integrated with Databricks' Lakehouse, enable efficient retrieval of contextual data for LLMs"("Building LLM Applications with Databricks").
* C. Conversation Buffer Memory: This component stores the conversation history, allowing the LLM to maintain context across multiple turns. It's essential for contextual memory.
* Databricks Reference:"Conversation Buffer Memory tracks prior user inputs and LLM outputs, ensuring context-aware responses"("Generative AI Engineer Guide").
* D. External tools: These (e.g., APIs or calculators) enhance functionality but aren't required for a basicchat app with the specified capabilities.
* E. Chat loaders: These might refer to data loaders for chat logs, but they're not a core chain component for conversational functionality or memory.
* F. React Components: These relate to front-end UI development, not the LLM chain's backend functionality.
* Selecting the Two Required Components:
* Forknowledge retrieval, Vector Stores (B) are necessary to fetch relevant external data, a cornerstone of Databricks' RAG-based chat systems.
* Forcontextual memory, Conversation Buffer Memory (C) is required to maintain conversation history, ensuring coherent and context-aware responses.
* While an LLM itself is implied as the core generator, the question asks for chain components beyond the model, making B and C the minimal yet sufficient pair for a basic application.
Conclusion: The two required chain components areB. Vector StoresandC. Conversation Buffer Memory, as they directly address knowledge retrieval and contextual memory, respectively, aligning with Databricks' documented best practices for LLM-enabled chat applications.

NEW QUESTION # 61
Which indicator should be considered to evaluate the safety of the LLM outputs when qualitatively assessing LLM responses for a translation use case?

A. The latency of the response and the length of text generated
B. The similarity to the previous language
C. The ability to generate responses in code
D. The accuracy and relevance of the responses

Answer: D

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 # 62
A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results.
How should they configure the endpoint to pass the secrets and credentials?

A. Pass variables using the Databricks Feature Store API
B. Add credentials using environment variables
C. Use spark.conf.set ()
D. Pass the secrets in plain text

Answer: B

Explanation:
Context: Deploying an application that uses an MLflow Pyfunc model involves managing sensitive information such as secrets and credentials securely.
Explanation of Options:
* Option A: Use spark.conf.set(): While this method can pass configurations within Spark jobs, using it for secrets is not recommended because it may expose them in logs or Spark UI.
* Option B: Pass variables using the Databricks Feature Store API: The Feature Store API is designed for managing features for machine learning, not for handling secrets or credentials.
* Option C: Add credentials using environment variables: This is a common practice for managing credentials in a secure manner, as environment variables can be accessed securely by applications without exposing them in the codebase.
* Option D: Pass the secrets in plain text: This is highly insecure and not recommended, as it exposes sensitive information directly in the code.
Therefore,Option Cis the best method for securely passing secrets and credentials to an application, protecting them from exposure.

NEW QUESTION # 63
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. Include in the system prompt that any information it sees will be about SnoPenAI, even if no data filtering is performed.
B. Keep all articles because the RAG application needs to understand non-company content to avoid answering questions about them.
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.

Answer: C

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.

NEW QUESTION # 64
......

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