Databricks-Generative-AI-Engineer-Associate Sample Questions Answers

Problem Context: The task is to design responses for a healthcare-focused chatbot that appropriately addresses the urgency of a patient's symptoms.

Explanation of Options:

Option A: Suggesting articles might be suitable for less urgent inquiries but is inappropriate for symptoms that could indicate a serious condition.

Option B: Given the description of severe symptoms like headaches and dizziness, directing the patient to emergency services is prudent. This aligns with medical guidelines that recommend immediate professional attention for such severe symptoms.

Option C: Offering well-wishes does not address the potential seriousness of the symptoms and lacks appropriate action.

Option D: While gathering more information is part of a detailed assessment, the immediate need here suggests a more urgent response.

Given the potential severity of the described symptoms,Option Bis the most appropriate, ensuring the chatbot directs patients to seek urgent care when needed, potentially saving lives.

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.

The task requires an LLM pipeline for multi-stage reasoning with external tools, necessitating planning, adaptability, and complex reasoning. Let’s evaluate the options based on Databricks’ recommendations for advanced LLM workflows.

Option A: Train the LLM to generate a single, comprehensive response without interacting with any external tools, relying solely on its pre-trained knowledge

This approach limits the LLM to its static knowledge base, excluding external tools and multi-stage reasoning. It can’t adapt or plan actions dynamically, failing the requirements.

Databricks Reference:"External tools enhance LLM capabilities beyond pre-trained knowledge"("Building LLM Applications with Databricks," 2023).

Option B: Implement a framework like ReAct which allows the LLM to generate reasoning traces and perform task-specific actions that leverage external tools if necessary

ReAct (Reasoning + Acting) combines reasoning traces (step-by-step logic) with actions (e.g., tool calls), enabling the LLM to plan, adapt, and execute complex tasks iteratively. This meets all requirements: multi-stage reasoning, tool use, and adaptability.

Databricks Reference:"Frameworks like ReAct enable LLMs to interleave reasoning and external tool interactions for complex problem-solving"("Generative AI Cookbook," 2023).

Option C: Encourage the LLM to make multiple API calls in sequence without planning or structuring the calls, allowing the LLM to decide when and how to use external tools spontaneously

Unstructured, spontaneous API calls lack planning and may lead to inefficient or incorrect tool usage. This doesn’t ensure effective multi-stage reasoning or adaptability.

Databricks Reference: Structured frameworks are preferred:"Ad-hoc tool calls can reduce reliability in complex tasks"("Building LLM-Powered Applications").

Option D: Use a Chain-of-Thought (CoT) prompting technique to guide the LLM through a series of reasoning steps, then manually input the results from external tools for the final answer

CoT improves reasoning but relies on manual tool interaction, breaking automation and adaptability. It’s not a scalable pipeline solution.

Databricks Reference:"Manual intervention is impractical for production LLM pipelines"("Databricks Generative AI Engineer Guide").

Conclusion: Option B (ReAct) is the best approach, as it integrates reasoning and tool use in a structured, adaptive framework, aligning with Databricks’ guidance for complex LLM workflows.

When prioritizing cost and latency over quality in a Large Language Model (LLM)-based application, it is crucial to select a configuration that minimizes both computational resources and latency while still providing reasonable performance. Here's whyDis the best choice:

Context length: The context length of 512 tokens aligns with the chunk size used for the documents (maximum of 512 tokens per chunk). This is sufficient for capturing the needed information and generating responses without unnecessary overhead.

Smallest model size: The model with a size of 0.13GB is significantly smaller than the other options. This small footprint ensures faster inference times and lower memory usage, which directly reduces both latency and cost.

Embedding dimension: While the embedding dimension of 384 is smaller than the other options, it is still adequate for tasks where cost and speed are more important than precision and depth of understanding.

This setup achieves the desired balance between cost-efficiency and reasonable performance in a latency-sensitive, cost-conscious application.

To build an LLM-powered system that accesses up-to-date news articles and stock prices, the best approach is tocreate an agentthat 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.

In the context of ensuring that outputs are relevant to financial news, increasing compute power (option B) does not directly improve therelevanceof the LLM-generated outputs. Here’s why:

Compute Power and Relevancy:Increasing compute power can help the model process inputs faster, but it does not inherently improve therelevanceof the answers. Relevancy depends on the data sources, the retrieval method, and the filtering mechanisms in place, not on how quickly the model processes the query.

What Actually Helps with Relevance:Other methods, like content filtering, guardrails, or manual review, can directly impact the relevance of the model’s responses by ensuring the model focuses on pertinent financial content. These methods help tailor the LLM’s responses to the financial domain and avoid irrelevant or harmful outputs.

Why Other Options Are More Relevant:

A (Comprehensive Guardrail Framework): This will ensure that the model avoids generating content that is irrelevant or inappropriate in the finance sector.

C (Profanity Filter): While not directly related to financial relevancy, ensuring the output is clean and professional is still important in maintaining the quality of responses.

D (Manual Review): Incorporating human oversight to catch and correct issues with the LLM’s output ensures the final answers are aligned with financial content expectations.

Thus, increasing compute power does not help with ensuring the outputs are more relevant to financial news, making option B the correct answer.

In this scenario, the Generative AI Engineer needs to design a system that can handle different types of queries about the monster truck team. The queries may involve text-based information, API lookups for event dates, or table queries for standings. The best solution is to implement atool-based agent system.

Here’s how option B works, and why it’s the most appropriate answer:

System Design Using Agent-Based Model:In modern agent-based LLM systems, you can design a system where the LLM (Large Language Model) acts as a central orchestrator. The model can "decide" which tools to use based on the query. These tools can include API calls, table lookups, or natural language searches. The system should contain asystem promptthat informs the LLM about the available tools.

System Prompt Listing Tools:By creating a well-craftedsystem prompt, the LLM knows which tools are at its disposal. For instance, one tool may query an external API for event dates, another might look up standings in a database, and a third may involve searching a vector database for general text-based information. Theagentwill be responsible for calling the appropriate tool depending on the query.

Agent Orchestration of Calls:The agent system is designed to execute a series of steps based on the incoming query. If a user asks for the next event date, the system will recognize this as a task that requires an API call. If the user asks about standings, the agent might query the appropriate table in the database. For text-based questions, it may call a search function over ingested data. The agent orchestrates this entire process, ensuring the LLM makes calls to the right resources dynamically.

Generative AI Tools and Context:This is a standard architecture for integrating multiple functionalities into a system where each query requires different actions. The core design in option B is efficient because it keeps the system modular and dynamic by leveraging tools rather than overloading the LLM with static information in a system prompt (like option D).

Why Other Options Are Less Suitable:

A (RAG Architecture): While relevant, simply ingesting PDFs into a vector store only helps with text-based retrieval. It wouldn’t help with API lookups or table queries.

C (Conditional Logic with RAG/API/TABLE): Although this approach works, it relies heavily on manual text parsing and might introduce complexity when scaling the system.

D (System Prompt with Event Dates and Standings): Hardcoding dates and table information into a system prompt isn’t scalable. As the standings or events change, the system would need constant updating, making it inefficient.

By bundling multiple tools into a single agent-based system (as in option B), the Generative AI Engineer can best handle the diverse requirements of this system.

The task is to choose between LSH and HNSW for a vector database index, prioritizing semantic accuracy. The evaluation must assess how well each method retrieves semantically relevant results. Let’s evaluate the options.

Option A: Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs

Cosine similarity measures semantic closeness between vectors, directly assessing retrieval accuracy in a vector database. Comparing returned results’ embeddings to test inputs’ embeddings evaluates how well LSH or HNSW preserves semantic relationships, aligning with the priority.

Databricks Reference:"Cosine similarity is a standard metric for evaluating vector search accuracy"("Databricks Vector Search Documentation," 2023).

Option B: Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs

BLEU evaluates text generation (e.g., translations), not vector retrieval accuracy. It’s irrelevant for indexing performance.

Databricks Reference:"BLEU applies to generative tasks, not retrieval"("Generative AI Cookbook").

Option C: Compare the Recall-Oriented-Understudy for Gisting Evaluation (ROUGE) scores of returned results for a representative sample of test inputs

ROUGE is for summarization evaluation, not vector search. It doesn’t measure semantic accuracy in retrieval.

Databricks Reference:"ROUGE is unsuited for vector database evaluation"("Building LLM Applications with Databricks").

Option D: Compare the Levenshtein distances of returned results against a representative sample of test inputs

Levenshtein distance measures string edit distance, not semantic similarity in embeddings. It’s inappropriate for vector-based retrieval.

Databricks Reference: No specific support for Levenshtein in vector search contexts.

Conclusion: Option A (cosine similarity) is the correct approach, directly evaluating semantic accuracy in vector retrieval, as recommended by Databricks for Vector Search assessments.

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 abasicchat 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:

Forknowledge retrieval, Vector Stores (B) are necessary to fetch relevant external data, a cornerstone of Databricks’ RAG-based chat systems.

Forcontextual 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 areB. Vector StoresandC. Conversation Buffer Memory, as they directly address knowledge retrieval and contextual memory, respectively, aligning with Databricks’ documented best practices for LLM-enabled chat applications.

For a small, cost-conscious startup in the cancer research field, choosing a domain-specific and smaller LLM is the most effective strategy. Here's whyBis the best choice:

Domain-specific performance: A smaller LLM that has been fine-tuned for the domain of cancer research will outperform a general-purpose LLM for specialized queries. This ensures high-quality responses without needing to rely on a large, expensive LLM.

Cost-efficiency: Smaller models are cheaper to run, both in terms of compute resources and API usage costs. A domain-specific smaller LLM can deliver good quality responses without the need for the extensive computational power required by larger models.

Focused knowledge: In a specialized field like cancer research, having an LLM tailored to the subject matter provides better relevance and accuracy for queries, while keeping costs low. Large, general-purpose LLMs may provide irrelevant information, leading to inefficiency and higher costs.

This approach allows the startup to balance quality, cost, and customer satisfaction effectively, making it the most suitable strategy.

In this case, the Generative AI Engineer is developing an application to generate personalized birthday poems, but there’s a need to safeguard againstmalicious user inputs. The best solution is to implement asafety filter(option A) to detect harmful or inappropriate inputs.

Safety Filter Implementation:Safety filters are essential for screening user input and preventing inappropriate content from being processed by the LLM. These filters can scan inputs for harmful language, offensive terms, or malicious content and intervene before the prompt is passed to the LLM.

Graceful Handling of Harmful Inputs:Once the safety filter detects harmful content, the system can provide a message to the user, such as "I'm unable to assist with this request," instead of processing or responding to malicious input. This protects the system from generating harmful content and ensures a controlled interaction environment.

Why Other Options Are Less Suitable:

B (Reduce Interaction Time): Reducing the interaction time won’t prevent malicious inputs from being entered.

C (Continue the Conversation): While it’s possible to acknowledge malicious input, it is not safe to continue the conversation with harmful content. This could lead to legal or reputational risks.

D (Increase Compute Power): Adding more compute doesn’t address the issue of harmful content and would only speed up processing without resolving safety concerns.

Therefore, implementing asafety filterthat blocks harmful inputs is the most effective technique for safeguarding the application.

To fix the error in the LangChain code provided for using a simple prompt template, the correct approach is Option C. Here's a detailed breakdown of why Option C is the right choice and how it addresses the issue:

Proper Initialization: In Option C, the LLMChain is correctly initialized with the LLM instance specified as OpenAI(), which likely represents a language model (like GPT) from OpenAI. This is crucial as it specifies which model to use for generating responses.

Correct Use of Classes and Methods:

The PromptTemplate is defined with the correct format, specifying that adjective is a variable within the template. This allows dynamic insertion of values into the template when generating text.

The prompt variable is properly linked with the PromptTemplate, and the final template string is passed correctly.

The LLMChain correctly references the prompt and the initialized OpenAI() instance, ensuring that the template and the model are properly linked for generating output.

Why Other Options Are Incorrect:

Option A: Misuses the parameter passing in generate method by incorrectly structuring the dictionary.

Option B: Incorrectly uses prompt.format method which does not exist in the context of LLMChain and PromptTemplate configuration, resulting in potential errors.

Option D: Incorrect order and setup in the initialization parameters for LLMChain, which would likely lead to a failure in recognizing the correct configuration for prompt and LLM usage.

Thus, Option C is correct because it ensures that the LangChain components are correctly set up and integrated, adhering to proper syntax and logical flow required by LangChain's architecture. This setup avoids common pitfalls such as type errors or method misuses, which are evident in other options.

In the context of designing a chatbot to engage users on a gaming platform,diversity of responses(option B) is a key metric to increase user engagement and retention. Here’s why:

Diverse and Engaging Interactions:A chatbot that provides varied and interesting responses will keep users engaged, especially inan interactive environment like a gaming platform. Gamers typically enjoy dynamic and evolving conversations, anddiversity of responseshelps prevent monotony, encouraging users to interact more frequently with the bot.

Increasing Retention:By offering different types of responses to similar queries, the chatbot can create a sense of novelty and excitement, which enhances the user’s experience and makes them more likely to return to the platform.

Why Other Options Are Less Effective:

A (Randomness): Random responses can be confusing or irrelevant, leading to frustration and reducing engagement.

C (Lack of Relevance): If responses are not relevant to the user’s queries, this will degrade the user experience and lead to disengagement.

D (Repetition of Responses): Repetitive responses can quickly bore users, making the chatbot feel uninteresting and reducing the likelihood of continued interaction.

Thus,diversity of responses(option B) is the most effective way to keep users engaged and retain them on the platform.

The task is to deploy an LLM application using Foundation Model APIs in a production environment while adhering to security best practices. Authentication is critical for securing access to Databricks resources, such as the Foundation Model API. Let’s evaluate the options based on Databricks’ security guidelines for production scenarios.

Option A: Use an access token belonging to service principals

Service principals are non-human identities designed for automated workflows and applications in Databricks. Using an access token tied to a service principal ensures that the authentication is scoped to the application, follows least-privilege principles (via role-based access control), and avoids reliance on individual user credentials. This is a security best practice for production deployments.

Databricks Reference:"For production applications, use service principals with access tokens to authenticate securely, avoiding user-specific credentials"("Databricks Security Best Practices," 2023). Additionally, the "Foundation Model API Documentation" states:"Service principal tokens are recommended for programmatic access to Foundation Model APIs."

Option B: Use a frequently rotated access token belonging to either a workspace user or a service principal

Frequent rotation enhances security by limiting token exposure, but tying the token to a workspace user introduces risks (e.g., user account changes, broader permissions). Including both user and service principal options dilutes the focus on application-specific security, making this less ideal than a service-principal-only approach. It also adds operational overhead without clear benefits over Option A.

Databricks Reference:"While token rotation is a good practice, service principals are preferred over user accounts for application authentication"("Managing Tokens in Databricks," 2023).

Option C: Use OAuth machine-to-machine authentication

OAuth M2M (e.g., client credentials flow) is a secure method for application-to-service communication, often using service principals under the hood. However, Databricks’ Foundation Model API primarily supports personal access tokens (PATs) or service principal tokens over full OAuth flows for simplicity in production setups. OAuth M2M adds complexity (e.g., managing refresh tokens) without a clear advantage in this context.

Databricks Reference:"OAuth is supported in Databricks, but service principal tokens are simpler and sufficient for most API-based workloads"("Databricks Authentication Guide," 2023).

Option D: Use an access token belonging to any workspace user

Using a user’s access token ties the application to an individual’s identity, violating security best practices. It risks exposure if the user leaves, changes roles, or has overly broad permissions, and it’s not scalable or auditable for production.

Databricks Reference:"Avoid using personal user tokens for production applications due to security and governance concerns"("Databricks Security Best Practices," 2023).

Conclusion: Option A is the best choice, as it uses a service principal’s access token, aligning with Databricks’ security best practices for production LLM applications. It ensures secure, application-specific authentication with minimal complexity, as explicitly recommended for Foundation Model API deployments.

The Generative AI Engineer needs to follow a methodical pipeline to build and deploy a Retrieval-Augmented Generation (RAG) application. The steps outlined in optionBaccurately reflect this process:

Ingest documents from a source: This is the first step, where the engineer collects documents (e.g., technical regulations) that will be used for retrieval when the application answers user questions.

Index the documents and save to Vector Search: Once the documents are ingested, they need to be embedded using a technique like embeddings (e.g., with a pre-trained model like BERT) and stored in a vector database (such as Pinecone or FAISS). This enables fast retrieval based on user queries.

User submits queries against an LLM: Users interact with the application by submitting their queries. These queries will be passed to the LLM.

LLM retrieves relevant documents: The LLM works with the vector store to retrieve the most relevant documents based on their vector representations.

LLM generates a response: Using the retrieved documents, the LLM generates a response that is tailored to the user's question.

Evaluate model: After generating responses, the system must be evaluated to ensure the retrieved documents are relevant and the generated response is accurate. Metrics such as accuracy, relevance, and user satisfaction can be used for evaluation.

Deploy it using Model Serving: Once the RAG pipeline is ready and evaluated, it is deployed using a model-serving platform such as Databricks Model Serving. This enables real-time inference and response generation for users.

By following these steps, the Generative AI Engineer ensures that the RAG application is both efficient and effective for the task of answering technical regulation questions.