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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	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 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.
Topic 4	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.

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Databricks Certified Generative AI Engineer Associate Sample Questions (Q23-Q28):

NEW QUESTION # 23
A Generative AI Engineer is deploying a conversational agent that provides financial-analysis insights to internal analysts at a company. The company has standardized on Microsoft 365 for all internal communications and collaboration. The analysts are distributed across multiple time zones and primarily work within Microsoft Teams channels, where they already discuss financial data and share reports. The agent needs to integrate with the company's existing Azure Active Directory for authentication and must respect the company's data-governance policies. Users should be able to interact with the agent within their existing workflow without switching applications.
Which interface should the engineer implement for this agent deployment?

A. Microsoft Teams integration to deploy the agent as a bot within Teams channels where analysts already collaborate.
B. REST API endpoint with a custom frontend application hosted on the company's internal portal.
C. Slack integration to enable the agent in a dedicated workspace for the financial-analysis team.
D. Databricks Apps to create a custom web application that analysts can access through their browsers with SSO authentication.

Answer: A

Explanation:
Microsoft Teams integration directly satisfies the requirement to use the agent within the analysts' existing collaboration environment. Users can ask questions where they already discuss financial information, avoiding a separate portal or browser application. Databricks documents Teams integration that allows users to ask data questions through direct messages, channels, or group chats. Authentication and downstream authorization must still be configured correctly; selecting Teams does not automatically grant access to governed data. Slack conflicts with the company's standardized communication platform. A custom frontend or Databricks App can provide an effective interface, but both introduce an additional destination when the requirement explicitly prioritizes staying in Teams. The deployment should connect the Teams interface to the appropriate governed agent backend. Databricks documentation

NEW QUESTION # 24
A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results.
How should they configure the endpoint to pass the secrets and credentials?

A. Pass variables using the Databricks Feature Store API
B. Pass the secrets in plain text
C. Use spark.conf.set ()
D. Add credentials using environment variables

Answer: D

NEW QUESTION # 25
An AI developer team wants to fine-tune an open-weight model to have exceptional performance on a code generation use case. They are trying to choose the best model to start with. They want to minimize model hosting costs and are using Hugging Face model cards and spaces to explore models. Which TWO model attributes and metrics should the team focus on to make their selection?

A. Chatbot Arena Leaderboard
B. Number of model downloads last month
C. Number of model parameters
D. MTEB Leaderboard
E. Big Code Models Leaderboard

Answer: C,E

Explanation:
To optimize for code generation performance and hosting costs, a Generative AI engineer must look at specific metrics.
Big Code Models Leaderboard (A): This is the industry-standard benchmark for code-specific LLMs (like StarCoder or CodeLlama). It measures performance on tasks like HumanEval and MBPP, providing a direct indicator of how well the model handles programming logic.
Number of model parameters (B): This is the primary driver of hosting costs. Larger models (e.g., 70B) require more GPU memory (VRAM) and more expensive compute instances (like A100s/H100s) than smaller models (e.g., 7B or 13B). To minimize costs, the team should look for the smallest model that achieves a high score on the Big Code Leaderboard.
Note: MTEB (C) is for embeddings, and Chatbot Arena (D) is for general-purpose chat, neither of which is the primary metric for specialized code generation fine-tuning.

NEW QUESTION # 26
A Generative Al Engineer is building a production-ready LLM system which replies directly to customers. The solution makes use of the Foundation Model API via provisioned throughput. They are concerned that the LLM could potentially respond in a toxic or otherwise unsafe way. They also wish to perform this with the least amount of effort.
Which approach will do this?

A. Add a regex expression on inputs and outputs to detect unsafe responses.
B. Ask users to report unsafe responses
C. Add some LLM calls to their chain to detect unsafe content before returning text
D. Host Llama Guard on Foundation Model API and use it to detect unsafe responses

Answer: D

Explanation:
The task is to prevent toxic or unsafe responses in an LLM system using the Foundation Model API with minimal effort. Let's assess the options.
Option A: Host Llama Guard on Foundation Model API and use it to detect unsafe responses Llama Guard is a safety-focused model designed to detect toxic or unsafe content. Hosting it via the Foundation Model API (a Databricks service) integrates seamlessly with the existing system, requiring minimal setup (just deployment and a check step), and leverages provisioned throughput for performance.
Databricks Reference: "Foundation Model API supports hosting safety models like Llama Guard to filter outputs efficiently" ("Foundation Model API Documentation," 2023).
Option B: Add some LLM calls to their chain to detect unsafe content before returning text Using additional LLM calls (e.g., prompting an LLM to classify toxicity) increases latency, complexity, and effort (crafting prompts, chaining logic), and lacks the specificity of a dedicated safety model.
Databricks Reference: "Ad-hoc LLM checks are less efficient than purpose-built safety solutions" ("Building LLM Applications with Databricks").
Option C: Add a regex expression on inputs and outputs to detect unsafe responses Regex can catch simple patterns (e.g., profanity) but fails for nuanced toxicity (e.g., sarcasm, context-dependent harm), requiring significant manual effort to maintain and update rules.
Databricks Reference: "Regex-based filtering is limited for complex safety needs" ("Generative AI Cookbook").
Option D: Ask users to report unsafe responses
User reporting is reactive, not preventive, and places burden on users rather than the system. It doesn't limit unsafe outputs proactively and requires additional effort for feedback handling.
Databricks Reference: "Proactive guardrails are preferred over user-driven monitoring" ("Databricks Generative AI Engineer Guide").
Conclusion: Option A (Llama Guard on Foundation Model API) is the least-effort, most effective approach, leveraging Databricks' infrastructure for seamless safety integration.

NEW QUESTION # 27
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?

A. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.
B. Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.
C. Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.
D. Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.

Answer: A

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 # 28
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