Pass the Databricks Generative AI Engineer Databricks-Generative-AI-Engineer-Associate Questions and answers with CertsForce

In this scenario, the chatbot must avoid answering political questions and instead provide a standard message for such inquiries. Implementing aSafety Guardrailis the appropriate solution for this:

What is a Safety Guardrail?Safety guardrails are mechanisms implemented in Generative AI systems to ensure the model behaves within specific bounds. In this case, it ensures the chatbot does not answer politically sensitive or irrelevant questions, which aligns with the business rules.

Preventing Responses to Political Questions:The Safety Guardrail is programmed to detect specific types of inquiries (like political questions) and prevent the model from generating responses outside its intended domain. When such queries are detected, the guardrail intervenes and provides a pre-defined response: “Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance.”

How It Works in Practice:The LLM system can include aclassification layeror trigger rules based on specific keywords related to politics. When such terms are detected, the Safety Guardrail blocks the normal generation flow and responds with the fixed message.

Why Other Options Are Less Suitable:

B (Security Guardrail): This is more focused on protecting the system from security vulnerabilities or data breaches, not controlling the conversational focus.

C (Contextual Guardrail): While context guardrails can limit responses based on context, safety guardrails are specifically about ensuring the chatbot stays within a safe conversational scope.

D (Compliance Guardrail): Compliance guardrails are often related to legal and regulatory adherence, which is not directly relevant here.

Therefore, aSafety Guardrailis the right framework to ensure the chatbot only answers insurance-related queries and avoids political discussions.

The task is to benchmark LLMs for text translation using an evaluation set with known high-quality examples, requiring a performant metric. Let’s evaluate the options.

Option A: ROUGE metric

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures overlap between generated and reference texts, primarily for summarization. It’s less suited for translation, where precision and word order matter more.

Databricks Reference:"ROUGE is commonly used for summarization, not translation evaluation"("Generative AI Cookbook," 2023).

Option B: BLEU metric

BLEU (Bilingual Evaluation Understudy) evaluates translation quality by comparing n-gram overlap with reference translations, accounting for precision and brevity. It’s widely used, performant, and appropriate for this task.

Databricks Reference:"BLEU is a standard metric for evaluating machine translation, balancing accuracy and efficiency"("Building LLM Applications with Databricks").

Option C: NDCG metric

NDCG (Normalized Discounted Cumulative Gain) assesses ranking quality, not text generation. It’s irrelevant for translation evaluation.

Databricks Reference:"NDCG is suited for ranking tasks, not generative output scoring"("Databricks Generative AI Engineer Guide").

Option D: RECALL metric

Recall measures retrieved relevant items but doesn’t evaluate translation quality (e.g., fluency, correctness). It’s incomplete for this use case.

Databricks Reference: No specific extract, but recall alone lacks the granularity of BLEU for text generation tasks.

Conclusion: Option B (BLEU) is the best metric for translation evaluation, offering a performant and standard approach, as endorsed by Databricks’ guidance on generative tasks.

The task described in the question involves enhancing customer experience by automatically handling common customer inquiries using an LLM-powered AI solution. This requires the system to process input data (customer inquiries) and generate personalized, relevant responses efficiently. Let’s evaluate the options step-by-step in the context of Databricks Generative AI Engineer principles, which emphasize leveraging LLMs for tasks like question answering, summarization, and retrieval-augmented generation (RAG).

Option A: Input: Customer reviews; Output: Group the reviews by users and aggregate per-user average rating, then respond

This option focuses on analyzing customer reviews to compute average ratings per user. While this might be useful for sentiment analysis or user profiling, it does not directly address the goal of handling common customer inquiries or improving response times for personalized interactions. Customer reviews are typically feedback data, not real-time inquiries requiring immediate responses.

Databricks Reference: Databricks documentation on LLMs (e.g., "Building LLM Applications with Databricks") emphasizes that LLMs excel at tasks like question answering and conversational responses, not just aggregation or statistical analysis of reviews.

Option B: Input: Customer service chat logs; Output: Group the chat logs by users, followed by summarizing each user's interactions, then respond

This option uses chat logs as input, which aligns with customer service scenarios. However, the output—grouping by users and summarizing interactions—focuses on user-specific summaries rather than directly addressing inquiries. While summarization is an LLM capability, this approach lacks the specificity of finding answers to common questions, which is central to the problem.

Databricks Reference: Per Databricks’ "Generative AI Cookbook," LLMs can summarize text, but for customer service, the emphasis is on retrieval and response generation (e.g., RAG workflows) rather than user interaction summaries alone.

Option C: Input: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary

This option uses chat logs (real customer inquiries) as input and tasks the LLM with identifying answers to similar questions, then providing a summarized response. This directly aligns with the goal of handling common inquiries efficiently while maintaining personalization (by referencing past interactions or similar cases). It leverages LLM capabilities like semantic search, retrieval, and response generation, which are core to Databricks’ LLM workflows.

Databricks Reference: From Databricks documentation ("Building LLM-Powered Applications," 2023), an exact extract states:"For customer support use cases, LLMs can be used to retrieve relevant answers from historical data like chat logs and generate concise, contextually appropriate responses."This matches Option C’s approach of finding answers and summarizing them.

Option D: Input: Customer reviews; Output: Classify review sentiment

This option focuses on sentiment classification of reviews, which is a valid LLM task but unrelated to handling customer inquiries or improving response times in a conversational context. It’s more suited for feedback analysis than real-time customer service.

Databricks Reference: Databricks’ "Generative AI Engineer Guide" notes that sentiment analysis is a common LLM task, but it’s not highlighted for real-time conversational applications like customer support.

Conclusion: Option C is the best fit because it uses relevant input (chat logs) and defines an LLM task (finding answers and summarizing) that meets the requirements of improving response times and maintaining personalized interaction. This aligns with Databricks’ recommended practices for LLM-powered customer service solutions, such as retrieval-augmented generation (RAG) workflows.

Problem Context: The Generative AI Engineer needs a prompt that will enable an LLM trained on customer call transcripts to classify and respond correctly regarding product availability. The desired response should clearly indicate whether a product is "In Stock" or "Out of Stock," and it should be formatted in a way that is structured and easy to parse programmatically, such as JSON.

Explanation of Options:

Option A: Respond with “In Stock” if the customer asks for a product. This prompt is too generic and does not specify how to handle the case when a product is not available, nor does it provide a structured output format.

Option B: This option is correctly formatted and explicit. It instructs the LLM to respond based on the availability mentioned in the customer call transcript and to format the response in JSON. This structure allows for easy integration into systems that may need to process this information automatically, such as customer service dashboards or databases.

Option C: Respond with “Out of Stock” if the customer asks for a product. Like option A, this prompt is also insufficient as it only covers the scenario where a product is unavailable and does not provide a structured output.

Option D: While this prompt correctly specifies how to respond based on product availability, it lacks the structured output format, making it less suitable for systems that require formatted data for further processing.

Given the requirements for clear, programmatically usable outputs,Option Bis the optimal choice because it provides precise instructions on how to respond and includes a JSON format example for structuring the output, which is ideal for automated systems or further data handling.

The goal is to make fine-tuned LLMs from a production (prod) Databricks workspace available for testing in a development (dev) workspace, leveraging Unity Catalog and MLflow, while ensuring cost-effectiveness and security. Let’s analyze the options.

Option A: Use an external model registry which can be accessed from all workspaces

An external registry adds cost (e.g., hosting fees) and complexity (e.g., integration, security configurations) outside Databricks’ native ecosystem, reducing security compared to Unity Catalog’s governance.

Databricks Reference:"Unity Catalog provides a centralized, secure model registry within Databricks"("Unity Catalog Documentation," 2023).

Option B: Setup a script to export the model from prod and import it to dev

Export/import scripts require manual effort, storage for model artifacts, and repeated execution, increasing operational cost and risk (e.g., version mismatches, unsecured transfers). It’s less efficient than a native solution.

Databricks Reference: Manual processes are discouraged when Unity Catalog offers built-in sharing:"Avoid redundant workflows with Unity Catalog’s cross-workspace access"("MLflow with Unity Catalog").

Option C: Setup a duplicate training pipeline in dev, so that an identical model is available in dev

Duplicating the training pipeline doubles compute and storage costs, as it retrains the model from scratch. It’s neither cost-effective nor necessary when the prod model can be reused securely.

Databricks Reference:"Re-running training is resource-intensive; leverage existing models where possible"("Generative AI Engineer Guide").

Option D: Use MLflow to log the model directly into Unity Catalog, and enable READ access in the dev workspace to the model

Unity Catalog, integrated with MLflow, allows models logged in prod to be centrally managed and accessed across workspaces with fine-grained permissions (e.g., READ for dev). This is cost-effective (no extra infrastructure or retraining) and secure (governed by Databricks’ access controls).

Databricks Reference:"Log models to Unity Catalog via MLflow, then grant access to other workspaces securely"("MLflow Model Registry with Unity Catalog," 2023).

Conclusion: Option D leverages Databricks’ native tools (MLflow and Unity Catalog) for a seamless, cost-effective, and secure solution, avoiding external systems, manual scripts, or redundant training.

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.

Problem Context: The engineer needs to extract text from PDF documents, which may contain both text and images. The goal is to find a Python package that simplifies this task using the least amount of code.

Explanation of Options:

Option A: flask: Flask is a web framework for Python, not suitable for processing or extracting content from PDFs.

Option B: beautifulsoup: Beautiful Soup is designed for parsing HTML and XML documents, not PDFs.

Option C: unstructured: This Python package is specifically designed to work with unstructured data, including extracting text from PDFs. It provides functionalities to handle various types of content in documents with minimal coding, making it ideal for the task.

Option D: numpy: Numpy is a powerful library for numerical computing in Python and does not provide any tools for text extraction from PDFs.

Given the requirement,Option C(unstructured) is the most appropriate as it directly addresses the need to efficiently extract text from PDF documents with minimal code.

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.

The LLM classifies mushroom species accurately but includes unwanted reasoning text, and the engineer wants only the label. Let’s assess how to control output format effectively.

Option A: Use few shot prompting to instruct the model on expected output format

Few-shot prompting provides examples (e.g., input: description, output: label). It can work but requires crafting multiple examples, which is effort-intensive and less direct than a clear instruction.

Databricks Reference:"Few-shot prompting guides LLMs via examples, effective for format control but requires careful design"("Generative AI Cookbook").

Option B: Use zero shot prompting to instruct the model on expected output format

Zero-shot prompting relies on a single instruction (e.g., “Return only the label”) without examples. It’s simpler than few-shot but may not consistently enforce succinctness if the LLM’s default behavior is verbose.

Databricks Reference:"Zero-shot prompting can specify output but may lack precision without examples"("Building LLM Applications with Databricks").

Option C: Use zero shot chain-of-thought prompting to prevent a verbose output format

Chain-of-Thought (CoT) encourages step-by-step reasoning, which increases verbosity—opposite to the desired outcome. This contradicts the goal of label-only output.

Databricks Reference:"CoT prompting enhances reasoning but often results in detailed responses"("Databricks Generative AI Engineer Guide").

Option D: Use a system prompt to instruct the model to be succinct in its answer

A system prompt (e.g., “Respond with only the species label, no additional text”) sets a global instruction for the LLM’s behavior. It’s direct, reusable, and effective for controlling output style across queries.

Databricks Reference:"System prompts define LLM behavior consistently, ideal for enforcing concise outputs"("Generative AI Cookbook," 2023).

Conclusion: Option D is the most effective and straightforward action, using a system prompt to enforce succinct, label-only responses, aligning with Databricks’ best practices for output control.

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.