Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Test 3 — 15 Questions

Question 1

When evaluating an LLM for deployment, what is a crucial 'non-functional' requirement that must be addressed?

Accepted Answer

The inference latency and throughput under expected load.. Non-functional requirements like latency, throughput, and cost are as important as the model's accuracy. In production, an accurate model that is too slow to provide a response is useless. Similarly, a high-performing model that is prohibitively expensive to run is not viable. Balancing these factors is essential for ensuring that the generative AI application is both technically feasible and financially sustainable at scale.

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The model's ability to learn from user feedback automatically.

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The number of parameters in the model architecture.

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The language in which the model was initially trained.

Question 2

A machine learning engineer needs to deploy a custom Mosaic AI Model Serving endpoint that requires access to a private internal database. Which mechanism should be used to securely manage the database credentials?

Accepted Answer

Reference the credentials using the Databricks Secrets API within the model loading script.. Databricks Secrets provide a centralized, secure way to store and reference sensitive information like database credentials. By using the secret scope, the engineer avoids hardcoding sensitive data into the model code or configuration files. This practice is essential for maintaining a secure MLOps lifecycle, as it prevents credential exposure and allows for granular access control via Databricks access control lists (ACLs) on the secret scope itself.

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Hardcode the credentials as environment variables in the model serving endpoint deployment configuration.

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Inject the credentials via a public GitHub repository linked to the Databricks Git folder.

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Store the credentials in a plain-text file on the Unity Catalog volume attached to the serving endpoint.

Question 3

An organization is deploying an LLM application using Databricks Asset Bundles (DABs). Which TWO of the following are primary benefits of using DABs for the deployment process?

Accepted Answer

Enables infrastructure-as-code (IaC) for Databricks resources.. Databricks Asset Bundles streamline the development and deployment lifecycle by treating infrastructure and code as a single unit. They facilitate consistent environments across development, staging, and production by using YAML-based configuration files. This ensures that the deployment process is reproducible, automated via CI/CD pipelines, and manageable through version control, reducing human error and configuration drift in complex ML-driven applications.

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Automatic generation of unit tests for Python code.

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Provides a drag-and-drop interface for building pipelines.

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Automatically migrates all legacy data to Unity Catalog.

Question 4

Refer to the exhibit. An engineer is automating the deployment of a model to an endpoint using the Databricks CLI. Based on the error log provided, what is the most appropriate action to resolve this deployment failure?

Accepted Answer

Grant the service principal 'USE' and 'READ' permissions on the catalog, schema, and model.. Deployment failures due to 'INSUFFICIENT_PERMISSIONS' often stem from the service principal used by the CI/CD pipeline lacking the necessary grants on the registered model in Unity Catalog. The engineer must ensure the principal has 'USE CATALOG', 'USE SCHEMA', and 'READ' permissions on the model version. Addressing this at the Unity Catalog level is the standard procedure for cross-service authorization in Databricks.

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Upgrade the model serving endpoint to a higher capacity cluster size.

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Delete the model serving endpoint and recreate it using a personal access token.

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Restart the Databricks workspace to force an update of the internal permission cache.

Question 5

Which component of the Databricks platform allows you to bundle together notebooks, model serving configurations, and pipeline definitions for repeatable deployment?

Accepted Answer

Databricks Asset Bundles.. Databricks Asset Bundles (DABs) are the standardized way to manage and deploy project artifacts. By bundling these components, teams can maintain version control over their entire application ecosystem. This approach is fundamental for implementing robust CI/CD pipelines, ensuring that the development, testing, and production environments remain synchronized and predictable throughout the release cycle.

Answer

Databricks SQL Warehouse.

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Unity Catalog.

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MLflow Tracking.

Question 6

When deploying a model to Databricks Model Serving, which THREE of the following are best practices to ensure production reliability?

Accepted Answer

Configuring autoscaling policies based on traffic patterns.. Production reliability for model serving requires careful attention to environment configuration, scalability, and security. Best practices include using specific model versions to prevent unexpected updates, configuring autoscaling to handle variable traffic, and utilizing private connectivity to keep data transmission secure. These steps minimize downtime and ensure that the serving endpoint behaves predictably under various load conditions, which is critical for enterprise-grade generative AI applications.

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Deploying the 'latest' alias for all production endpoints.

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Using static credentials embedded directly in the deployment YAML.

Question 7

Which workflow best describes the recommended CI/CD process for updating a Databricks Asset Bundle?

Accepted Answer

Update the local YAML, run 'bundle validate', and deploy to the target environment.. A robust CI/CD workflow for Databricks Asset Bundles involves validating the configuration, testing in a development workspace, and then deploying to production via an automated process. By validating the bundle before deployment, you catch syntax errors early. This pipeline-driven approach ensures that all changes are tracked in version control, reviewed through pull requests, and deployed consistently, minimizing the risks associated with manual workspace configuration changes.

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Make changes directly in the production workspace and export the updated YAML.

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Use the Databricks UI to update the Job definition and update the YAML manually later.

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Deploy the bundle to production without validation to speed up the delivery time.

Question 8

What is the primary purpose of the 'bundle validate' command in the Databricks Asset Bundles CLI?

Accepted Answer

To check for schema compliance and configuration errors.. The 'bundle validate' command performs a schema check and structural analysis on your configuration files. It ensures that the YAML is syntactically correct and that all referenced resources exist or will be created correctly. This step is crucial for preventing common errors before you attempt a deployment, saving time and avoiding partially successful deployments that could leave the environment in an inconsistent state.

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To execute the deployment and verify it in the workspace.

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To format the YAML files according to Databricks standards.

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To automatically update the bundle version to the latest release.

Question 9

Refer to the exhibit. An engineer wants to perform a canary deployment by routing 10% of traffic to a new version (version 6). How should the JSON traffic configuration be modified?

Accepted Answer

Add a new route for version 6 and set its traffic_percentage to 10, then set version 5 to 90.. Canary deployments involve splitting traffic between an established model version and a new candidate. To achieve this, the 'routes' list must include both versions, with the 'traffic_percentage' values summing to 100%. This controlled rollout allows teams to monitor the performance and accuracy of the new model on a small segment of production traffic before committing the full load to the new version.

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Set the traffic_percentage of version 6 to 10 and keep version 5 at 100.

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Remove version 5 and set version 6 to 10.

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Create a new endpoint entirely for version 6 and switch the DNS record.

Question 10

Which of the following is a primary reason to prefer Databricks Asset Bundles (DABs) over manual workspace deployment?

Accepted Answer

DABs allow for version-controlled and automated deployments.. The primary advantage of DABs is the elimination of manual, error-prone configuration. By defining infrastructure as code, teams gain repeatability, version control, and automation. This leads to significantly fewer deployment failures and allows for consistent, auditable environments across development and production. It is a fundamental shift toward mature MLOps practices, where infrastructure is as controlled and tested as the application code itself.

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DABs provide better performance for Spark queries.

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DABs automatically convert your code to SQL.

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DABs require less memory to run than the standard workspace.

Question 11

An AI engineer is developing a custom Databricks App using Mosaic AI Agent Framework and needs to deploy the application workspace securely. Which deployment artifact and configuration mechanism should the engineer use to define the app dependencies and entry point?

Accepted Answer

An app.yaml configuration file defining the command, environment variables, and source path alongside the application source code.. Deploying Databricks Apps requires an app.yaml configuration file and source code bundled together. The app.yaml file specifies the runtime, environment variables, and command-line entry points required to execute the application properly inside the Databricks environment. Defining these parameters correctly ensures the Mosaic AI Agent dependencies are initialized and served without manual intervention.

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A setup.py file placed in the root directory configured to automatically trigger a cluster restart upon deployment.

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A Databricks Asset Bundles deployment target pointing to an external Kubernetes cluster hosting the agent code.

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A standard requirements.txt file executed through a Jupyter notebook scheduled via a workflow job.

Question 12

A machine learning engineer needs to deploy a real-time Model Serving endpoint. Which Databricks construct is required to manage the model's environment, dependencies, and artifacts while ensuring version control for the deployment?

Accepted Answer

MLflow Models within Unity Catalog. Databricks Model Serving relies on the Unity Catalog Model Registry to manage model versions and artifacts. By using Unity Catalog, organizations ensure governance, lineage, and consistent environment reproduction. This is critical for production deployments as it decouples the model development lifecycle from the serving infrastructure, ensuring that the exact artifact deployed in staging is the one running in production, thereby maintaining model integrity and reproducibility across environments.

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Delta Live Tables pipelines

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Databricks Job clusters

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Workspace-local Model Registry

Question 13

A data team is developing a Mosaic AI Agent application. Which TWO of the following are mandatory for deploying this application using the Databricks Model Serving infrastructure?

Accepted Answer

A logged model signature defining input and output schemas. Deploying Mosaic AI Agents requires defining a signature for the model input/output and ensuring the agent code is packaged with its dependencies. These steps are crucial because the Serving environment needs to understand how to route requests and instantiate the environment. Properly defining the signature ensures type safety during inference, while dependency management prevents runtime errors, ensuring the agent operates as expected within the serverless environment.

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A manual provisioning of a dedicated GPU worker pool

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A hardcoded API key for external vector store access

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A pre-configured Kubernetes manifest file

Question 14

Refer to the exhibit. An engineer is configuring a canary deployment for a churn prediction model. Based on the provided traffic configuration, what is the expected behavior of the endpoint?

Accepted Answer

90% of requests are routed to churn_v1 and 10% to churn_v2.. The configuration implements a traffic-splitting strategy, directing 90% of requests to 'churn_v1' and 10% to 'churn_v2'. This is a standard pattern for A/B testing or canary releases, allowing teams to validate new models in production with minimal risk. By monitoring the performance of the 10% traffic slice, engineers can decide whether to promote the model, ensuring stability before a full rollout.

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The endpoint will error because total traffic must equal 100% for each model version individually.

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The endpoint will only route traffic to churn_v1 after churn_v2 reaches capacity.

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The endpoint will round-robin requests between v1 and v2 regardless of the weights.

Question 15

When deploying an application using Databricks Asset Bundles (DABs), which file is the primary entry point to define the project structure and configuration?

Accepted Answer

databricks.yml. Databricks Asset Bundles use a YAML-based configuration file, typically named 'databricks.yml', to define the project's resources, targets, and settings. This file acts as the single source of truth for the project's lifecycle, enabling consistent deployments across development, staging, and production environments. By using a declarative configuration, teams can version-control their entire infrastructure, ensuring that deployment patterns are standardized and repeatable across the organization.

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requirements.txt

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setup.py

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bundle.json