Databricks-GenAI-Assoc Design Applications — Timed 10 Questions

Question 1

Refer to the exhibit. An engineer is configuring a model serving endpoint. Based on the configuration, which outcome is expected during a sudden surge in traffic?

Accepted Answer

The endpoint will scale up to 16 CPU cores to handle the increased load.. The configuration enables auto-scaling, which allows the endpoint to dynamically adjust resources between the specified CPU limits. This capability is vital for managing variable workloads, ensuring that the application remains responsive during traffic spikes while optimizing infrastructure costs during idle periods. This is a core component of production-ready Databricks generative AI model serving design.

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The endpoint will crash due to lack of static resource allocation.

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The endpoint will force requests to queue indefinitely until traffic subsides.

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The configuration is invalid because it lacks a GPU definition.

Question 2

Which THREE practices are recommended when designing a robust Databricks Model Serving endpoint for a high-traffic generative AI application?

Accepted Answer

Enabling auto-scaling to handle variable traffic demand.. Deploying a high-traffic generative AI application requires careful endpoint configuration, including resource limits, security, and observability. By setting appropriate scaling limits, using private endpoints, and enabling monitoring, engineers ensure the system is both performant and secure. These practices are essential for managing the unpredictable nature of AI traffic and ensuring that the service meets enterprise standards for availability and data protection.

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Configuring the endpoint to be publicly accessible from the internet.

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Allocating infinite CPU resources to prevent any latency.

Question 3

Which TWO architectural patterns are best practices when designing a scalable RAG application on Databricks to ensure data security and performance?

Accepted Answer

Use Unity Catalog to manage access control on the source Delta tables powering the vector index.. By decoupling the embedding generation from the retrieval process and enforcing Unity Catalog permissions, you ensure that the application is secure and performs efficiently. These patterns are essential for enterprise applications where data privacy is paramount and high concurrency is expected. Proper indexing strategies combined with granular access control ensure that users only retrieve information they are authorized to view during the generative process.

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Store all sensitive data in cleartext within the vector index for faster retrieval.

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Perform embedding generation as a batch process at the time of retrieval.

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Expose the LLM endpoint directly to the public internet without authentication.

Question 4

Which THREE components are required to monitor the quality and performance of a RAG application hosted on Databricks?

Accepted Answer

MLflow Tracing for capturing the entire chain of inference requests.. Monitoring RAG applications requires a holistic view of the retrieval, generation, and infrastructure layers. By tracking retrieval metrics, LLM performance, and system latency, engineers can identify bottlenecks and optimize the system. These components are vital for maintaining the accuracy and reliability of the RAG pipeline, ensuring the model's output remains relevant and helpful for the end users over time.

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A manual spreadsheet to log every user interaction for offline review.

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A hardcoded threshold for total token usage per day.

Question 5

Which Databricks feature allows you to securely manage LLM prompts and keep them versioned as code?

Accepted Answer

MLflow Models. MLflow is the standard tool in Databricks for managing the lifecycle of machine learning models and associated assets, including prompts. By versioning prompts in MLflow, teams can collaborate effectively, track changes, and roll back if a new prompt version causes quality issues. This is essential for maintaining a high-quality, reproducible generative AI application in a production environment.

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Databricks Repos

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Unity Catalog Volumes

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Delta Lake Constraints

Question 6

Which THREE strategies should be implemented to mitigate hallucination in a RAG application?

Accepted Answer

Incorporate a 'self-correction' step where the model reviews its own output.. Hallucination is a common challenge in LLM applications. By grounding the model with high-quality retrieval, setting strict temperature parameters, and implementing systematic evaluation, developers can significantly reduce inaccurate outputs. These strategies ensure the LLM remains constrained by the provided context and does not deviate into factual errors, which is critical for trustworthy enterprise deployments.

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Set the LLM temperature to a high value like 1.0 or greater.

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Increase the number of retrieved chunks to include more irrelevant noise.

Question 7

An engineer is designing a Databricks RAG chain using LangChain on a Databricks cluster. The chain must call an external embedding model through a Databricks Model Serving endpoint and must persist intermediate retrieval results for debugging failed conversations. Compliance requires that no raw user question text leave the workspace boundary except through the approved serving endpoint. Which design decision best satisfies the persistence and boundary requirements together?

Accepted Answer

Use MLflow Tracing with the Databricks tracking server to capture the chain spans, including retrieved documents and prompts. MLflow Tracing is the native Databricks mechanism for capturing the internal steps of a GenAI chain, producing structured spans for retrieval, prompt construction, and model calls. Because traces land in the workspace's MLflow experiment, engineers get detailed debugging data without routing raw user text to an external system, satisfying the compliance boundary while preserving observability.

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Write raw question and answer pairs to a Delta table using a custom callback handler registered in the chain

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Log the final prompt string to the driver's stdout and rely on cluster log delivery for retention

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Stream all chain events to an external observability SaaS platform using its LangChain integration

Question 8

A GenAI engineer is designing a retrieval pipeline that uses Databricks Vector Search with hybrid search enabled. Users report that semantically similar but keyword-distinct queries return irrelevant chunks, and that results vary between runs of the same query. Which TWO configuration choices should the engineer make to improve result relevance and consistency? (Choose two.)

Accepted Answer

Configure the index to return only results whose similarity score exceeds a high static threshold, discarding everything below it.. Hybrid retrieval activated through query_text blends keyword and vector signals, and a reranker plus a similarity threshold tightens and stabilizes the final candidate set. Returning unfiltered extra results, seeding embeddings, or dropping keyword matching either fails to address ranking quality or misidentifies the cause of inconsistent results.

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Disable hybrid search and rely solely on dense vector similarity, since keyword matching introduces nondeterministic scoring.

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Set a fixed random seed on the embedding model so that repeated queries always produce identical similarity scores.

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Increase the number of results returned by the index and pass all of them, unchanged, directly into the LLM prompt as context.

Question 9

A GenAI engineer is building a RAG chatbot on Databricks. The application must answer questions over a 40 GB internal knowledge base and must return the first token to users within one second. Retrieval latency is currently dominated by network round trips between the serving endpoint and the vector store. Which design change best reduces end-to-end latency while keeping retrieval quality intact?

Accepted Answer

Co-locate the Vector Search index and the model serving endpoint in the same Databricks workspace and region, and enable the endpoint's built-in query optimization.. The latency problem is caused by network round trips between the serving endpoint and the vector index. Placing both resources in the same Databricks workspace and region removes those hops, and the endpoint's optimized query path to the local index keeps retrieval quality intact. Scaling, embedding size, or billing model changes do not remove the network bottleneck.

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Enable Foundation Model APIs pay-per-token for the chat model and keep the Vector Search index in a separate workspace.

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Configure the serving endpoint to use scale-to-zero so idle replicas are eliminated during low-traffic periods.

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Increase the embedding dimensions of the Vector Search index from 1024 to 3072 to improve retrieval precision.

Question 10

A GenAI engineer is designing a RAG chatbot on Databricks that must answer questions from a 2 million-document knowledge base with sub-second retrieval latency. The team wants to avoid re-embedding the entire corpus on every model upgrade and wants the index to stay synchronized with a Delta table that receives near-real-time updates. Which design choice best satisfies these requirements?

Accepted Answer

Create a Databricks Vector Search Delta Sync index on the source Delta table and configure a continuous sync mode.. The Delta Sync index in Databricks Vector Search is the only option that provides a managed, low-latency vector index that automatically stays synchronized with a Delta table. Continuous sync mode addresses near-real-time updates without custom pipelines, and the index decouples embedding generation from query serving so model upgrades can be handled by re-embedding or rebuilding the index rather than changing application code.

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Store embeddings in a Unity Catalog volume and use a Python dictionary cached on the driver for lookups.

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Export the corpus to Parquet files and use a Spark UDF to compute cosine similarity at query time.

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Use MLflow Model Registry to version the embedding model and call the model serving endpoint directly for each document retrieval.