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| Section | Weight | Objectives |
|---|---|---|
| Data Preparation | 14% | - Manage data quality and format - Prepare and process data for LLM use - Implement data pipelines and transformations |
| Assembling and Deploying Apps | 22% | - Manage application lifecycle - Package and deploy applications - Deploy models via Model Serving |
| Application Development | 30% | - Build RAG applications - Integrate with MLflow - Use Databricks Vector Search - Develop LLM chains and workflows |
| Design Applications | 14% | - Decompose complex requirements into tasks - Design solution architecture - Select appropriate models, tools and approaches |
| Governance | 8% | - Apply Unity Catalog for data governance - Ensure compliance and security - Manage access and permissions |
| Evaluation and Monitoring | 12% | - Monitor application behavior and outputs - Evaluate model and application performance - Iterate and improve solutions |
>> Databricks-Generative-AI-Engineer-Associate Dumps Questions <<
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NEW QUESTION # 67
A Generative AI Engineer has a provisioned throughput model serving endpoint as part of a RAG application and would like to monitor the serving endpoint's incoming requests and outgoing responses. The current approach is to include a micro-service in between the endpoint and the user interface to write logs to a remote server.
Which Databricks feature should they use instead which will perform the same task?
Answer: B
Explanation:
Problem Context: The goal is to monitor theserving endpointfor incoming requests and outgoing responses in aprovisioned throughput model serving endpointwithin aRetrieval-Augmented Generation (RAG) application. The current approach involves using a microservice to log requests and responses to a remote server, but the Generative AI Engineer is looking for a more streamlined solution within Databricks.
Explanation of Options:
* Option A: Vector Search: This feature is used to perform similarity searches within vector databases.
It doesn't provide functionality for logging or monitoring requests and responses in a serving endpoint, so it's not applicable here.
* Option B: Lakeview: Lakeview is not a feature relevant to monitoring or logging request-response cycles for serving endpoints. It might be more related to viewing data in Databricks Lakehouse but doesn't fulfill the specific monitoring requirement.
* Option C: DBSQL: Databricks SQL (DBSQL) is used for running SQL queries on data stored in Databricks, primarily for analytics purposes. It doesn't provide the direct functionality needed to monitor requests and responses in real-time for an inference endpoint.
* Option D: Inference Tables: This is the correct answer.Inference Tablesin Databricks are designed to store the results and metadata of inference runs. This allows the system to logincoming requests and outgoing responsesdirectly within Databricks, making it an ideal choice for monitoring the behavior of a provisioned serving endpoint. Inference Tables can be queried and analyzed, enabling easier monitoring and debugging compared to a custom microservice.
Thus,Inference Tablesare the optimal feature for monitoring request and response logs within the Databricks infrastructure for a model serving endpoint.
NEW QUESTION # 68
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: 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 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 # 69
A Generative Al Engineer is building an LLM-based application that has an important transcription (speech-to-text) task. Speed is essential for the success of the application Which open Generative Al models should be used?
Answer: B
Explanation:
The task requires an open generative AI model for a transcription (speech-to-text) task where speed is essential. Let's assess the options based on their suitability for transcription and performance characteristics, referencing Databricks' approach to model selection.
Option A: Llama-2-70b-chat-hf
Llama-2 is a text-based LLM optimized for chat and text generation, not speech-to-text. It lacks transcription capabilities.
Databricks Reference: "Llama models are designed for natural language generation, not audio processing" ("Databricks Model Catalog").
Option B: MPT-30B-Instruct
MPT-30B is another text-based LLM focused on instruction-following and text generation, not transcription. It's irrelevant for speech-to-text tasks.
Databricks Reference: No specific mention, but MPT is categorized under text LLMs in Databricks' ecosystem, not audio models.
Option C: DBRX
DBRX, developed by Databricks, is a powerful text-based LLM for general-purpose generation. It doesn't natively support speech-to-text and isn't optimized for transcription.
Databricks Reference: "DBRX excels at text generation and reasoning tasks" ("Introducing DBRX," 2023)-no mention of audio capabilities.
Option D: whisper-large-v3 (1.6B)
Whisper, developed by OpenAI, is an open-source model specifically designed for speech-to-text transcription. The "large-v3" variant (1.6 billion parameters) balances accuracy and efficiency, with optimizations for speed via quantization or deployment on GPUs-key for the application's requirements.
Databricks Reference: "For audio transcription, models like Whisper are recommended for their speed and accuracy" ("Generative AI Cookbook," 2023). Databricks supports Whisper integration in its MLflow or Lakehouse workflows.
Conclusion: Only D. whisper-large-v3 is a speech-to-text model, making it the sole suitable choice. Its design prioritizes transcription, and its efficiency (e.g., via optimized inference) meets the speed requirement, aligning with Databricks' model deployment best practices.
NEW QUESTION # 70
A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot's focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message:
"Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance." Which framework type should be implemented to solve this?
Answer: A
Explanation:
In this scenario, the chatbot must avoid answering political questions and instead provide a standard message for such inquiries. Implementing a Safety Guardrail is the appropriate solution for this:
What is a Safety Guardrail?
Safety guardrails are mechanisms implemented in Generative AI systems to ensure the model behaves within specific bounds. In this case, it ensures the chatbot does not answer politically sensitive or irrelevant questions, which aligns with the business rules.
Preventing Responses to Political Questions:
The Safety Guardrail is programmed to detect specific types of inquiries (like political questions) and prevent the model from generating responses outside its intended domain. When such queries are detected, the guardrail intervenes and provides a pre-defined response: "Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance." How It Works in Practice:
The LLM system can include a classification layer or trigger rules based on specific keywords related to politics. When such terms are detected, the Safety Guardrail blocks the normal generation flow and responds with the fixed message.
Why Other Options Are Less Suitable:
B (Security Guardrail): This is more focused on protecting the system from security vulnerabilities or data breaches, not controlling the conversational focus.
C (Contextual Guardrail): While context guardrails can limit responses based on context, safety guardrails are specifically about ensuring the chatbot stays within a safe conversational scope.
D (Compliance Guardrail): Compliance guardrails are often related to legal and regulatory adherence, which is not directly relevant here.
Therefore, a Safety Guardrail is the right framework to ensure the chatbot only answers insurance-related queries and avoids political discussions.
NEW QUESTION # 71
A Generative AI Engineer I using the code below to test setting up a vector store:
Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?
Answer: C
Explanation:
Context : The Generative AI Engineer is setting up a vector store using Databricks ' VectorSearchClient.
This is typically done to enable fast and efficient retrieval of vectorized data for tasks like similarity searches.
Explanation of Options :
* Option A: vsc.get_index() : This function would be used to retrieve an existing index, not create one, so it would not be the logical next step immediately after creating an endpoint.
* Option B: vsc.create_delta_sync_index() : After setting up a vector store endpoint, creating an index is necessary to start populating and organizing the data. The create_delta_sync_index() function specifically creates an index that synchronizes with a Delta table, allowing automatic updates as the data changes. This is likely the most appropriate choice if the engineer plans to use dynamic data that is updated over time.
* Option C: vsc.create_direct_access_index() : This function would create an index that directly accesses the data without synchronization. While also a valid approach, it ' s less likely to be the next logical step if the default setup (typically accommodating changes) is intended.
* Option D: vsc.similarity_search() : This function would be used to perform searches on an existing index; however, an index needs to be created and populated with data before any search can be conducted.
Given the typical workflow in setting up a vector store, the next step after creating an endpoint is to establish an index, particularly one that synchronizes with ongoing data updates, hence Option B .
NEW QUESTION # 72
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