Databricks-GenAI-Assoc Application Development Practice Test 4 — 15 Questions

Question 1

A developer is deploying a custom LangChain agent application as a custom Python model to Databricks Model Serving. The application depends on a specific third-party library that is not included in the standard Databricks runtime environment. How should the developer ensure the dependency is installed when the model is loaded into the serving container?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Specify the required packages in the extra_pip_requirements argument when logging the MLflow model artifact.. When logging custom PyFunc models to MLflow, developers must explicitly specify Python dependencies using the extra_pip_requirements parameter or a conda.yaml file. This ensures that the Databricks Model Serving environment automatically provisions and installs all necessary external libraries during container builds, preventing runtime import errors and ensuring reliable model execution.

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Manually SSH into the serving container nodes after deployment and run pip install using terminal access.

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Include the external library installation command inside a standard print statement within the model scoring function.

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Rely on the serving endpoint to automatically discover and install any imported Python module dynamically from the public internet.

Question 2

Refer to the exhibit. An AI engineer is configuring a Databricks Asset Bundle (DAB) to deploy a generative AI application. When executing 'databricks bundle deploy --target prod', which workspace host will the bundle resources be deployed to, and why?

Accepted Answer

It will deploy to the production workspace at https://adb-987654321.azuredatabricks.net because the target flag explicitly selects the prod configuration block.. Databricks Asset Bundles allow developers to define multiple deployment targets within the databricks.yml configuration file. When the user explicitly passes the --target prod flag to the CLI command, the bundle execution context switches from the default target to the specified target configuration, overriding default settings and directing deployment to the production workspace host URL defined under the prod block.

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The deployment will fail because the default target conflicts with the production target definition during workspace authentication initialization.

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It will deploy to the development workspace because the dev target is explicitly marked with 'default: true'.

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It will deploy to both workspaces simultaneously to maintain synchronization between development and production environments.

Question 3

A developer is building a retrieval-augmented generation (RAG) application on Databricks. They need to ensure that embeddings are updated automatically when the underlying Delta table changes. Which approach is the most efficient and scalable?

Accepted Answer

Implement a Delta Live Tables pipeline using streaming tables and a Python UDF to generate embeddings on arrival.. Delta Live Tables (DLT) with streaming tables allows for continuous data processing and incremental updates. By integrating embedding generation directly into the pipeline, the developer ensures that the vector database stays in sync with the source data without manual intervention or complex scheduling. This architecture minimizes latency and improves data consistency, which is a foundational requirement for production-grade RAG applications within the Databricks ecosystem.

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Write a manual PySpark job that runs every hour to scan the entire Delta table and recompute all embeddings.

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Use a Databricks Job with a notebook task triggered by a cron schedule to process changes using a watermark.

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Deploy a Unity Catalog volume to store the embeddings and trigger an external API call from an event-driven function.

Question 4

Which TWO of the following are mandatory requirements for developing an AI application using the Databricks Mosaic AI Model Serving environment?

Accepted Answer

The model must be registered as a model version within a Unity Catalog schema.. Mosaic AI Model Serving requires specific configurations to ensure secure and performant access. Firstly, the model must be registered in Unity Catalog to maintain governance and lineage. Secondly, the serving endpoint requires defined compute resources, typically GPU-accelerated for LLMs, to handle inference requests. These requirements are essential for productionizing models, as they ensure that models are discoverable, governed, and have the necessary hardware to meet low-latency performance targets in real-time scenarios.

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The model must be stored in a legacy Databricks Workspace folder.

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The model must be served using a shared-access mode interactive cluster.

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The application code must perform manual model sharding across nodes.

Question 5

Refer to the exhibit. A developer wants to enable monitoring for their deployed LLM endpoint. Given the current configuration, what must the developer change to ensure that request/response logs are captured for analysis?

Accepted Answer

Update 'auto_capture_request_payload' to true.. The current configuration has 'auto_capture_request_payload' set to false, which prevents the logging of inference traffic. By updating this flag to true, the system will start capturing payloads into a Delta table. This is critical for monitoring model performance, drift, and quality. Enabling this feature allows data teams to perform retrospective analysis, which is essential for iterating on model prompts and fine-tuning configurations based on real user interactions.

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Change the task to 'llm/v1/completions'.

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Add a new route with 0% traffic to the 'traffic_config'.

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Increase the 'traffic_percentage' to 200%.

Question 6

Which Databricks feature is specifically designed to facilitate the rapid development and deployment of LLM applications by providing a managed environment for hosting and testing prompts?

Accepted Answer

Databricks Mosaic AI Playground. Databricks Mosaic AI Playground provides a low-code interface for developers to experiment with different LLMs and system prompts. It allows for quick iteration and testing of model behavior before moving into full production deployment. This environment is crucial because it bridges the gap between experimentation and application development, ensuring that developers can validate prompt engineering strategies within the same governed environment where their data and production models reside.

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Databricks SQL Warehouse

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Unity Catalog Volumes

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Delta Live Tables

Question 7

Which THREE practices are recommended when using MLflow for managing LLM experiments in Databricks?

Accepted Answer

Log model parameters such as temperature, top_p, and system prompts.. Effective experiment management requires traceability, reproducibility, and structured documentation. Logging parameters like temperature and system prompts ensures results can be replicated. Using signature definitions allows the system to validate inputs, reducing integration errors. Finally, tagging models aids in lifecycle management, allowing teams to distinguish between prototypes and production-ready versions. These practices ensure that teams can maintain high standards of rigor, enabling scalable AI operations within the enterprise environment.

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Store all model weights directly in the MLflow run object.

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Always delete old MLflow experiments to save storage space.

Question 8

A team needs to provide fine-grained access control to an LLM application that uses a vector index stored in a Delta table. Which Unity Catalog feature should they use to restrict access to specific rows based on user identity?

Accepted Answer

Row-level security (RLS). Row-level security (RLS) in Unity Catalog allows administrators to define policies that filter data returned from tables based on the user's identity. By applying these policies to the Delta table used as a knowledge base, the team ensures that the LLM only retrieves information the user is authorized to see. This is essential for compliance and security in multi-tenant or sensitive information application environments.

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Column-level masking

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Workspace-level permissions

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Access Control Lists (ACLs)

Question 9

Refer to the exhibit. The developer is attempting to log a custom model to the Unity Catalog. Which configuration is missing to ensure the model is registered correctly under the specified Unity Catalog location?

Accepted Answer

mlflow.set_registry_uri("databricks-uc"). To log a model to Unity Catalog, the registry must be specified as 'databricks-uc'. The default behavior for 'databricks' tracking URI is to log to the workspace model registry. By explicitly setting the registry to the Unity Catalog destination, the developer ensures the model is governed by the correct security and lineage frameworks, allowing for seamless downstream deployment and access control within the enterprise environment.

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mlflow.set_registry_uri("databricks")

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mlflow.set_tracking_uri("databricks-uc")

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mlflow.set_experiment("uc_experiment")

Question 10

When building an application that retrieves context from Databricks Vector Search, what is the recommended data format for storing the document chunks?

Accepted Answer

Delta tables. Delta tables are the native, optimized storage format for Databricks. When using Vector Search, the index is automatically maintained by Delta tables. This provides a performant and reliable way to sync data between the source Delta table and the vector index. Storing data in Delta ensures that the vector search index can benefit from CDC (Change Data Capture) patterns, keeping the RAG knowledge base automatically updated and consistent.

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JSON files in an S3 bucket

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Parquet files on DBFS root

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SQL Server database

Question 11

Which TWO actions are necessary to ensure that a model serving endpoint in Databricks remains available and performant during peak traffic hours?

Accepted Answer

Enable auto-scaling for the serving endpoint.. Maintaining performance requires both vertical and horizontal scaling strategies. First, configuring auto-scaling on the endpoint allows the infrastructure to adjust to fluctuating demand automatically. Second, ensuring the endpoint uses appropriate instance types—such as GPU-accelerated instances for LLMs—ensures the compute capacity is sufficient for inference tasks. These two actions are foundational for achieving high availability and low latency, preventing request timeouts and ensuring a smooth experience during heavy usage periods.

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Disable the serving endpoint's logging to save on compute cycles.

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Configure the endpoint to run on a single, fixed-size node to minimize latency.

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Manually restart the endpoint every hour to clear the cache.

Question 12

A data engineering team is using Databricks to prepare data for a RAG application. They want to ensure that document chunks are of consistent size and quality. Which tool should they use within the Databricks notebook environment to achieve this?

Accepted Answer

Distributed Spark UDFs with LangChain text splitters.. Databricks notebooks support a wide range of libraries, but for text processing, using Spark-native functions combined with libraries like LangChain allows for scalable and efficient chunking. By utilizing Spark's distributed processing capabilities, developers can process millions of documents in parallel. This is crucial for maintaining data quality in large-scale RAG systems, as it ensures that the context window of the LLM is not exceeded and that retrieval is focused and accurate.

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A standard Python for-loop on a single worker node.

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Manual data extraction into a local CSV file.

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Databricks SQL commands to manually truncate strings.

Question 13

Refer to the exhibit. A developer is registering a model. Why is the model signature, as shown in the exhibit, considered a best practice for model registration?

Accepted Answer

It provides automated input validation to improve service reliability.. Model signatures define the schema for inputs and outputs, allowing the serving layer to perform automated validation. When an incoming request doesn't match the signature, the serving infrastructure can reject it immediately, providing useful error messages. This prevents invalid data from reaching the model, reducing runtime errors and debugging time, and ensures that the application behaves predictably in production environments, which is critical for maintaining high service quality and reliability.

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It enables automatic data masking for sensitive strings.

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It automatically scales the number of serving nodes based on the prompt size.

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It forces the model to use a specific version of Python for execution.

Question 14

A company is scaling its RAG applications. Which THREE of the following are benefits of using Databricks Vector Search over managing a standalone vector database outside of the platform?

Accepted Answer

Automatic, continuous synchronization with Delta tables.. Databricks Vector Search offers benefits through tight integration, including automatic synchronization with Delta tables, which removes the need for custom ETL. Because it resides in the same security boundary, it inherits Unity Catalog's governance and access controls, ensuring data security. Finally, it uses the platform's managed infrastructure, eliminating the overhead of maintaining external database clusters, which allows developers to focus on application logic rather than infrastructure maintenance.

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Ability to use SQL as the only way to perform embedding calculations.

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Support for non-Databricks proprietary vector file formats.

Question 15

A developer needs to monitor the performance of an LLM application in production. They want to track the latency of their model endpoint. Where can they find this metric in the Databricks workspace?

Accepted Answer

In the 'Monitoring' tab of the Model Serving endpoint UI.. Databricks Model Serving provides built-in monitoring dashboards for every endpoint. These dashboards automatically track key performance indicators such as request latency, throughput, and error rates. Monitoring these metrics is vital for understanding the user experience and detecting potential performance bottlenecks. By providing this information natively, Databricks helps developers maintain high service quality without requiring external observability tools, streamlining the monitoring and optimization process for deployed models.

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In the Unity Catalog table lineage view.

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In the Databricks SQL Warehouse query history.

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In the workspace audit logs.