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Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.
Topic 2	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
Topic 3	Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.

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Databricks Certified Generative AI Engineer Associate Sample Questions (Q33-Q38):

NEW QUESTION # 33
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?

A. 1. Define the tools inside the agents 2. Load the agents into the LLM 3. Instruct the LLM to use COT reasoning to determine the appropriate agent
B. 1. Define or import the tools 2. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools
C. 1. Define the tools 2. Load each tool into a separate agent 3. Instruct the LLM to use ReAct to call the appropriate agent
D. 1. Define or import the tools 2. Add tools and LLM to the agent 3. Create the ReAct agent

Answer: B

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 # 34
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?

A. vsc.similarity_search()
B. vsc.get_index()
C. vsc.create_direct_access_index()
D. vsc.create_delta_sync_index()

Answer: D

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, henceOption B.

NEW QUESTION # 35
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?

A. Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.
B. Query the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.
C. Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.
D. Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime.

Answer: C

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 # 36
A team wants to serve a code generation model as an assistant for their software developers. It should support multiple programming languages. Quality is the primary objective.
Which of the Databricks Foundation Model APIs, or models available in the Marketplace, would be the best fit?

A. MPT-7b
B. Llama2-70b
C. BGE-large
D. CodeLlama-34B

Answer: D

Explanation:
For a code generation model that supports multiple programming languages and where quality is the primary objective,CodeLlama-34Bis the most suitable choice. Here's the reasoning:
* Specialization in Code Generation:CodeLlama-34B is specifically designed for code generation tasks.
This model has been trained with a focus on understanding and generating code, which makes it particularly adept at handling various programming languages and coding contexts.
* Capacity and Performance:The "34B" indicates a model size of 34 billion parameters, suggesting a high capacity for handling complex tasks and generating high-quality outputs. The large model size typically correlates with better understanding and generation capabilities in diverse scenarios.
* Suitability for Development Teams:Given that the model is optimized for code, it will be able to assist software developers more effectively than general-purpose models. It understands coding syntax, semantics, and the nuances of different programming languages.
* Why Other Options Are Less Suitable:
* A (Llama2-70b): While also a large model, it's more general-purpose and may not be as fine- tuned for code generation as CodeLlama.
* B (BGE-large): This model may not specifically focus on code generation.
* C (MPT-7b): Smaller than CodeLlama-34B and likely less capable in handling complex code generation tasks at high quality.
Therefore, for a high-quality, multi-language code generation application,CodeLlama-34B(option D) is the best fit.

NEW QUESTION # 37
A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author's web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user's query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.
Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)

A. Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric.
B. Pass known questions and best answers to an LLM and instruct the LLM to provide the best token count. Use a summary statistic (mean, median, etc.) of the best token counts to choose chunk size.
C. Add a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.
D. Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters.
Choose the strategy that gives the best performance metric.
E. Change embedding models and compare performance.

Answer: A,D

NEW QUESTION # 38
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