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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate Exam |
| Exam Number: | Databricks-Generative-AI-Engineer-Associate |
| Passing Score: | 700/1000 or 70% |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 2 years |
| Exam Price: | USD 200 |
| Real Exam Qty: | 45 |
| Available Languages: | English, Japanese, Brazilian Portuguese, Korean |
| Exam Format: | Multiple choice |
| Recommended Training: | Generative AI Engineering with Databricks |
| Exam Registration: | Databricks Certification Registration |
| Sample Questions: | Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions |
| Exam Way: | Online proctored or in-person test center |
| Pre Condition: | No formal prerequisites; recommended 6+ months hands-on experience building generative AI solutions |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/genai-engineer-associate |
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NEW QUESTION # 62
A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.
Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?
Answer: D
NEW QUESTION # 63
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 to create an agent that 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 # 64
Which TWO chain components are required for building a basic LLM-enabled chat application that includes conversational capabilities, knowledge retrieval, and contextual memory?
Answer: B,E
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 basic chat 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 :
* For knowledge retrieval , Vector Stores (B) are necessary to fetch relevant external data, a cornerstone of Databricks' RAG-based chat systems.
* For contextual 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 are B. Vector Stores and C. 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 # 65
A Generative AI Engineer is using LangGraph to define multiple tools in a single agentic application. They want to enable the main orchestrator LLM to decide on its own which tools are most appropriate to call for a given prompt. To do this, they must determine the general flow of the code. Which sequence will do this?
Answer: A
Explanation:
In modern agentic frameworks like LangGraph or LangChain, the standard workflow for creating an autonomous tool-calling agent follows a specific sequence. First, tools must be defined (often as Python functions with clear docstrings, which the LLM uses to understand the tool ' s purpose). Second, the agent logic is defined, which specifies how the LLM should think. Third, the agent is initialized using a logic pattern like ReAct (Reason + Act). The ReAct framework is essential here because it enables the " orchestrator " loop: the LLM receives a prompt, generates a " Thought " about which tool to use, generates an
" Action " to call that tool, receives an " Observation " (the tool ' s output), and repeats until it can provide a final answer. Loading tools into " separate agents " (C) or defining tools " inside " agents (D) are non-standard patterns that add unnecessary complexity and do not align with the centralized orchestration model required for LangGraph.
NEW QUESTION # 66
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: D
Explanation:
* Problem Context: The goal is to monitor the serving endpoint for incoming requests and outgoing responses in a provisioned throughput model serving endpoint within a Retrieval-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 Tables in Databricks are designed to store the results and metadata of inference runs. This allows the system to log incoming requests and outgoing responses directly 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 Tables are the optimal feature for monitoring request and response logs within the Databricks infrastructure for a model serving endpoint.
NEW QUESTION # 67
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