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

The task is to set up a Databricks Vector Search index for news articles, supporting queries like “monster truck news around January 5th, 1992,” with minimal effort. The index must filter by topic and a 10-day date range. Let’s evaluate the options.

Option A: Split articles by 10-day blocks and return the block closest to the query

Pre-splitting articles into 10-day blocks requires significant preprocessing and index management (e.g., one index per block). It’s effort-intensive and inflexible for dynamic date ranges.

Databricks Reference:"Static partitioning increases setup complexity; metadata filtering is preferred"("Databricks Vector Search Documentation").

Option B: Include metadata columns for article date and topic to support metadata filtering

Adding date and topic as metadata in the Vector Search index allows dynamic filtering (e.g., date ± 5 days, topic = “monster truck”) at query time. This leverages Databricks’ built-in metadata filtering, minimizing setup effort.

Databricks Reference:"Vector Search supports metadata filtering on columns like date or category for precise retrieval with minimal preprocessing"("Vector Search Guide," 2023).

Option C: Pass the query directly to the vector search index and return the best articles

Passing the full query (e.g., “Tell me about monster truck news around January 5th, 1992”) to Vector Search relies solely on embeddings, ignoring structured filtering for date and topic. This risks inaccurate results without explicit range logic.

Databricks Reference:"Pure vector similarity may not handle temporal or categorical constraints effectively"("Building LLM Applications with Databricks").

Option D: Create separate indexes by topic and add a classifier model to appropriately pick the best index

Separate indexes per topic plus a classifier model adds significant complexity (index creation, model training, maintenance), far exceeding “least effort.” It’s overkill for this use case.

Databricks Reference:"Multiple indexes increase overhead; single-index with metadata is simpler"("Databricks Vector Search Documentation").

Conclusion: Option B is the simplest and most effective solution, using metadata filtering in a single Vector Search index to handle date ranges and topics, aligning with Databricks’ emphasis on efficient, low-effort setups.

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.

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.

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.

When addressing concerns of hallucination and data leakage in an LLM application for internal company policies, fine-tuning the model on internal data with the hope it learns data boundaries can be problematic:

Risk of Data Leakage: Fine-tuning on sensitive or confidential data does not guarantee that the model will not inadvertently include or reference this data in its outputs. There’s a risk of overfitting to the specific data details, which might lead to unintended leakage.

Hallucination: Fine-tuning does not necessarily mitigate the model's tendency to hallucinate; in fact, it might exacerbate it if the training data is not comprehensive or representative of all potential queries.

Better Approaches:

A,C, andDinvolve setting up operational safeguards and constraints that directly address data leakage and ensure responses are aligned with specific user needs and security levels.

Fine-tuning lacks the targeted control needed for such sensitive applications and can introduce new risks, making it an unsuitable approach in this context.

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.

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.

 Context: The goal is to improve the online customer experience in a restaurant by handling common inquiries about bookings, minimizing escalations, and maintaining personalized interactions.

 Explanation of Options:

Option A: Grouping and summarizing chat logs by user could provide insights into customer interactions but does not directly address the task of handling booking inquiries or minimizing escalations.

Option B: Using chat logs to generate interactive buttons for booking details directly supports the goal of facilitating online bookings, minimizing the need for human intervention by providing clear, interactive options for customers to self-serve.

Option C: Classifying sentiment of customer reviews does not directly help with booking inquiries, although it might provide valuable feedback insights.

Option D: Providing cancellation options is helpful but narrowly focuses on one aspect of the booking process and doesn't support the broader goal of handling common inquiries about bookings.

Option Bbest supports the goal of improving online interactions by using chat logs to generate actionable items for customers, helping them complete booking tasks efficiently and reducing the need for human intervention.

 Problem Context: The goal is to parse emails to extract certain pieces of information and output this in a structured JSON format. Clarity and specificity in the prompt design will ensure higher accuracy in the LLM’s responses.

 Explanation of Options:

Option A: Provides a general guideline but lacks an example, which helps an LLM understand the exact format expected.

Option B: Includes a clear instruction and a specific example of the output format. Providing an example is crucial as it helps set the pattern and format in which the information should be structured, leading to more accurate results.

Option C: Does not specify that the output should be in JSON format, thus not meeting the requirement.

Option D: While it correctly asks for JSON format, it lacks an example that would guide the LLM on how to structure the JSON correctly.

Therefore,Option Bis optimal as it not only specifies the required format but also illustrates it with an example, enhancing the likelihood of accurate extraction and formatting by the LLM.