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

Topic	Details
Topic 1	Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.
Topic 2	Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM's response, LLM guardrails, and the best LLM based on the attributes of the application.
Topic 3	Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
Topic 4	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 5	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.

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Databricks Certified Generative AI Engineer Associate Sample Questions (Q67-Q72):

NEW QUESTION # 67
A company has a typical RAG-enabled, customer-facing chatbot on its website.

Select the correct sequence of components a user's questions will go through before the final output is returned. Use the diagram above for reference.

A. 1.embedding model, 2.vector search, 3.context-augmented prompt, 4.response-generating LLM
B. 1.response-generating LLM, 2.vector search, 3.context-augmented prompt, 4.embedding model
C. 1.context-augmented prompt, 2.vector search, 3.embedding model, 4.response-generating LLM
D. 1.response-generating LLM, 2.context-augmented prompt, 3.vector search, 4.embedding model

Answer: A

Explanation:
To understand how a typical RAG-enabled customer-facing chatbot processes a user's question, let's go through the correct sequence as depicted in the diagram and explained in option A:
Embedding Model (1):
The first step involves the user's question being processed through an embedding model. This model converts the text into a vector format that numerically represents the text. This step is essential for allowing the subsequent vector search to operate effectively.
Vector Search (2):
The vectors generated by the embedding model are then used in a vector search mechanism. This search identifies the most relevant documents or previously answered questions that are stored in a vector format in a database.
Context-Augmented Prompt (3):
The information retrieved from the vector search is used to create a context-augmented prompt. This step involves enhancing the basic user query with additional relevant information gathered to ensure the generated response is as accurate and informative as possible.
Response-Generating LLM (4):
Finally, the context-augmented prompt is fed into a response-generating large language model (LLM). This LLM uses the prompt to generate a coherent and contextually appropriate answer, which is then delivered as the final output to the user.
Why Other Options Are Less Suitable:
B, C, D: These options suggest incorrect sequences that do not align with how a RAG system typically processes queries. They misplace the role of embedding models, vector search, and response generation in an order that would not facilitate effective information retrieval and response generation.
Thus, the correct sequence is embedding model, vector search, context-augmented prompt, response-generating LLM, which is option A.

NEW QUESTION # 68
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 or import the tools 2. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools
B. 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
C. 1. Define or import the tools 2. Add tools and LLM to the agent 3. Create the ReAct agent
D. 1. Define the tools 2. Load each tool into a separate agent 3. Instruct the LLM to use ReAct to call the appropriate agent

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 # 69
A Generative AI Engineer needs to allocate costs for an agent deployed via Agent Framework behind AI Gateway. The finance team requires daily reporting by workspace and endpoint, including token usage and request counts. The engineer wants to query this data with SQL and join it to an internal cost-center mapping table.
Which table type best serves their needs?

A. Inference Tables.
B. AI Gateway usage table.
C. MLflow experiment run tables.
D. System Serving, Served Entities table.

Answer: B

Explanation:
The AI Gateway usage table is designed to capture operational consumption information for SQL-based reporting. Its records include identifiers for the workspace, endpoint, and request, together with token-usage information and timestamps. The engineer can aggregate consumption by day and endpoint, then join those results to the company's cost-center mapping. Request counting should respect the table's request and invocation identifiers because a single logical request can involve multiple inference calls. Served-entity metadata describes deployed entities rather than providing the primary consumption records. Inference tables emphasize request and response payloads, while MLflow runs organize experiments and evaluations. Token consumption supports cost allocation, but calculating actual monetary charges may additionally require applicable pricing or billing records. Databricks documentation

NEW QUESTION # 70
A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they're willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer's needs in this situation?

A. Llama2-70B
B. Dolly 1.5B
C. OpenAI GPT-4
D. BGE-large

Answer: D

Explanation:
Problem Context: The Generative AI Engineer needs a model for a Retrieval-Augmented Generation (RAG) application that provides high-quality answers, where latency and throughput are not major concerns. The key factors areconfidentialityandsensitivityof the data, as well as the requirement for all processing to be confined to internal resources without external data transmission.
Explanation of Options:
* Option A: Dolly 1.5B: This model does not typically support RAG applications as it's more focused on image generation tasks.
* Option B: OpenAI GPT-4: While GPT-4 is powerful for generating responses, its standard deployment involves cloud-based processing, which could violate the confidentiality requirements due to external data transmission.
* Option C: BGE-large: The BGE (Big Green Engine) large model is a suitable choice if it is configured to operate on-premises or within a secure internal environment that meets regulatory requirements.
Assuming this setup, BGE-large can provide high-quality answers while ensuring that data is not transmitted to third parties, thus aligning with the project's sensitivity and confidentiality needs.
* Option D: Llama2-70B: Similar to GPT-4, unless specifically set up for on-premises use, it generally relies on cloud-based services, which might risk confidential data exposure.
Given the sensitivity and confidentiality concerns,BGE-largeis assumed to be configurable for secure internal use, making it the optimal choice for this scenario.

NEW QUESTION # 71
A Generative AI Engineer is implementing a supervisor agent and two specialist agents in Databricks: a Sales Analyst for revenue questions and an HR Analyst for staff questions. Each specialist must retrieve data only from its own governed domain, and the engineer wants to preserve that separation using Databricks-native data access for each agent rather than building custom retrieval logic.
What should the engineer implement?

A. Create two separate Genie Spaces for Sales and HR, each scoped to its own governed datasets, and have each specialist agent call the appropriate Space through the API.
B. Create a single Genie Space over both domains and rely on the supervisor agent to route only sales questions to the Sales Analyst and HR questions to the HR Analyst.
C. Create a shared Genie Space over both domains, but use distinct service principals and Unity Catalog grants for each specialist agent's API access.
D. Create separate Knowledge Assistants for Sales and HR and have each specialist retrieve from the corresponding assistant.

Answer: A

Explanation:
Separate Genie Spaces provide domain-specific access to structured business datasets through Databricks- native conversational querying. The Sales specialist calls the Sales Space, and the HR specialist calls the HR Space. Each Space should contain the appropriate datasets and business context, with Unity Catalog permissions enforcing access to its underlying resources. Separating Spaces makes domain boundaries explicit and avoids relying only on an LLM's routing instructions. Knowledge Assistants primarily support document-based knowledge retrieval, making Genie the better fit for structured business questions. A shared Space with distinct permissions can enforce security, but separate Spaces better express the requested domain separation. The video's phrase "HR Analyst for stock questions" appears inconsistent; "staff questions" is an inferred typing correction. Databricks documentation

NEW QUESTION # 72
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