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

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.

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.

Problem Context: The engineer needs an efficient way to store chunks of unstructured documents to facilitate easy retrieval and search. The current dataframe consists of document filenames and associated text chunks.

Explanation of Options:

Option A: Splitting into train and test sets is more relevant for model training scenarios and not directly applicable to storage for retrieval in a Vector Search index.

Option B: Flattening the dataframe such that each row contains a single chunk with a unique identifier is the most performant for storage and retrieval. This structure aligns well with how data is indexed and queried in vector search applications, making it easier to retrieve specific chunks efficiently.

Option C: Creating a unique identifier for each document only does not address the need to access individual chunks efficiently, which is critical in a Vector Search application.

Option D: Storing each chunk as an independent JSON file creates unnecessary overhead and complexity in managing and querying large volumes of files.

OptionBis the most efficient and practical approach, allowing for streamlined indexing and retrieval processes in a Delta table environment, fitting the requirements of a Vector Search index.

For a code generation model that supports multiple programming languages and where quality is the primary objective,CodeLlama-34Bis 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.

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.

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.

Problem Context: When assessing the safety and effectiveness of LLM outputs in a translation use case, it is essential to ensure that the translations accurately and relevantly convey the intended message. The evaluation should focus on how well the LLM understands and processes different languages and contexts.

Explanation of Options:

Option A: The ability to generate responses in code– This is not relevant to translation quality or safety.

Option B: The similarity to the previous language– While ensuring that translations preserve the original's intent is important, this doesn't directly address the overall quality or safety of the translation.

Option C: The latency of the response and the length of text generated– These operational metrics are less critical in assessing the qualitative aspects of translation safety.

Option D: The accuracy and relevance of the responses– This is crucial in translation to ensure that the translated content is true to the original in meaning and appropriateness. Accuracy and relevance directly impact the effectiveness and safety of translations, especially in sensitive or nuanced contexts.

Thus,Option Dis the most important indicator when evaluating the safety of LLM outputs in translation, focusing on the core aspects that determine the utility and trustworthiness of translated content.

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 avector store(a database that stores vector representations of text). Each chunk of text is transformed into anembeddingusing 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.

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 involves building a question-answering chatbot for an automotive company using a database of vehicle-related documents. The chatbot must efficiently process customer inquiries and provide accurate responses. Let’s evaluate each component to determine which isnotuseful, per Databricks Generative AI Engineer principles.

Option A: Response-generating LLM

An LLM is essential for generating natural language responses to customer queries based on retrieved information. This is a core component of any chatbot.

Databricks Reference:"The response-generating LLM processes retrieved context to produce coherent answers"("Building LLM Applications with Databricks," 2023).

Option B: Invite users to submit long, rather than concise, questions

Encouraging long questions is a user interaction design choice, not a technical component of the chatbot’s architecture. Moreover, long, verbose questions can complicate intent detection and retrieval, reducing efficiency and accuracy—counter to best practices for chatbot design. Concise questions are typically preferred for clarity and performance.

Databricks Reference: While not explicitly stated, Databricks’ "Generative AI Cookbook" emphasizes efficient query processing, implying that simpler, focused inputs improve LLM performance. Inviting long questions doesn’t align with this.

Option C: Vector database

A vector database stores embeddings of the vehicle documents, enabling fast retrieval of relevant information via semantic search. This is critical for a question-answering system with a large document corpus.

Databricks Reference:"Vector databases enable scalable retrieval of context from large datasets"("Databricks Generative AI Engineer Guide").

Option D: Embedding model

An embedding model converts text (documents and queries) into vector representations for similarity search. It’s a foundational component for retrieval-augmented generation (RAG) in chatbots.

Databricks Reference:"Embedding models transform text into vectors, facilitating efficient matching of queries to documents"("Building LLM-Powered Applications").

Conclusion: Option B is not a usefulcomponentin building the chatbot. It’s a user-facing suggestion rather than a technical building block, and it could even degrade performance by introducing unnecessary complexity. Options A, C, and D are all integral to a Databricks-aligned chatbot architecture.