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

Topic	Details
Topic 1	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.
Topic 2	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 3	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 4	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
Topic 5	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.

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Databricks Certified Generative AI Engineer Associate Sample Questions (Q88-Q93):

NEW QUESTION # 88
Which of the following statements accurately identifies differences between the evaluation phase and the monitoring phase in the Generative AI application lifecycle within Databricks?

A. The evaluation phase uses Mosaic AI Agent Evaluation and an evaluation dataset to assess an agent's performance metrics and traces, while the monitoring phase relies on inference tables as source data for creating a metrics profile.
B. The evaluation phase ensures the agent's responses comply with business rules in production, whereas the monitoring phase is focused on SLA and performance metrics.
C. The evaluation phase uses all inference history to assess agent performance and readiness for production, while the monitoring phase uses only new inference-table records to monitor performance.
D. The evaluation phase logs and traces live API calls in production, while the monitoring phase runs metrics on inference tables containing those traces.

Answer: A

Explanation:
A best describes the dataset-based evaluation and inference-table-based monitoring workflow referenced in the question. Evaluation runs an application against selected test cases, collects traces, and calculates metrics to compare versions or detect regressions. Production monitoring examines real usage over time to identify operational or quality problems. Evaluation does not require all historical inference records, and monitoring is not limited to latency or service-level metrics; it can also assess response quality and policy adherence.
Option B incorrectly treats production tracing as the defining evaluation activity. Current MLflow 3 monitoring can apply scorers directly to production traces, so inference-table metrics profiles are one documented monitoring architecture rather than a universal requirement for every modern Databricks deployment. Evaluation documentation , monitoring documentation

NEW QUESTION # 89
A Generative AI Engineer received the following business requirements for an external chatbot.
The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event.
What is an ideal workflow for such a chatbot?

A. The chatbot should only process payments
B. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it's an upcoming event question, send the query to a text-to-SQL model. If it's about ticket purchasing, the customer should be redirected to a payment platform.
C. There should be two different chatbots handling different types of user queries.
D. The chatbot should only look at previous event information

Answer: B

Explanation:
* Problem Context: The chatbot must handle various types of queries and intelligently route them to the appropriate responses or systems.
* Explanation of Options:
* Option A: Limiting the chatbot to only previous event information restricts its utility and does not meet the broader business requirements.
* Option B: Having two separate chatbots could unnecessarily complicate user interaction and increase maintenance overhead.
* Option C: Implementing a multi-step workflow where the chatbot first identifies the type of question and then routes it accordingly is the most efficient and scalable solution. This approach allows the chatbot to handle a variety of queries dynamically, improving user experience and operational efficiency.
* Option D: Focusing solely on payments would not satisfy all the specified user interaction needs, such as inquiring about event details.
Option Coffers a comprehensive workflow that maximizes the chatbot's utility and responsiveness to different user needs, aligning perfectly with the business requirements.

NEW QUESTION # 90
A Generative AI Engineer is building a Databricks-hosted assistant that must (1) query Unity Catalog tables with row and column permissions enforced, and (2) avoid managing any external infrastructure. The team wants the LLM to use governed data access through tools exposed via MCP.
Which MCP server choice meets these constraints?

A. Run a custom MCP server on a self-managed VM that proxies the Unity Catalog API.
B. Use an external community MCP server for SQL and pass Unity Catalog tokens in prompts.
C. Use a managed Databricks MCP server integrated with Unity Catalog.
D. Expose JDBC directly to the model and enforce permissions in application code.

Answer: C

Explanation:
A managed Databricks MCP server provides tools for accessing Databricks services without requiring the team to host an external server. For structured table queries, a suitable managed SQL or Genie tool uses Databricks-governed data access. Unity Catalog remains responsible for authorization instead of delegating security decisions to the language model. When permissions must reflect the individual requester, the application must use the appropriate user authorization configuration so row filters and column masks are evaluated for that identity. Passing credentials in prompts exposes secrets unnecessarily. A self-managed virtual machine violates the infrastructure requirement. Direct JDBC access also fails the requested MCP architecture and introduces custom integration work. Managed hosting simplifies deployment while preserving the platform's access-control model. Databricks tools documentation , authentication documentation

NEW QUESTION # 91
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 2. Load each tool into a separate agent 3. Instruct the LLM to use ReAct to call the appropriate agent
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. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools
D. 1. Define or import the tools 2. Add tools and LLM to the agent 3. Create the ReAct agent

Answer: C

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 # 92
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. CodeLlama-34B
B. Llama2-70b
C. MPT-7b
D. BGE-large

Answer: A

Explanation:
For a code generation model that supports multiple programming languages and where quality is the primary objective, CodeLlama-34B is 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 # 93
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