Databricks-GenAI-Assoc Practice Test 3 — 10 Questions

Question 1

Which TWO factors are most important when selecting a chunking strategy for text data prior to vectorization?

Accepted Answer

The maximum input token length of the target embedding model.. Selecting a chunking strategy requires balancing semantic context and technical limitations like context window size. If chunks are too small, they lack meaning; if too large, they exceed the limits of the embedding model and introduce irrelevant noise. Proper chunking is vital for maximizing the accuracy of RAG systems, as it determines the granularity of the information available for the retriever to present to the LLM.

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The file format of the source documents (e.g., PDF vs. Word).

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The total number of documents in the corpus.

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The storage cost of the resulting vector database.

Question 2

Which approach is recommended for managing secrets, such as API keys for external LLM providers, in Databricks?

Accepted Answer

Storing keys in Databricks Secrets.. Databricks Secrets provides a secure, encrypted storage mechanism for sensitive information, preventing credentials from being hardcoded in notebooks or scripts. This is a foundational security best practice. By using scope-based secret management, you ensure that only authorized users or service principals can access the credentials, keeping your RAG application secure and compliant with enterprise security standards.

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Hardcoding keys in a configuration file within a Repo.

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Passing keys as plain text arguments in a job parameter.

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Storing keys in a public S3 bucket with restricted access.

Question 3

Refer to the exhibit. A developer is deploying a model using the provided JSON configuration. What is the primary benefit of setting 'scale_to_zero_enabled' to true in this production RAG application?

Accepted Answer

It reduces operational costs by shutting down compute resources when the endpoint is not serving traffic.. Setting 'scale_to_zero_enabled' to true allows the Databricks Model Serving endpoint to automatically shut down compute resources when no requests are being processed. This is highly beneficial for cost optimization, as it eliminates idle runtime costs during periods of inactivity. When a new request arrives, the service automatically initializes the model, ensuring cost efficiency without requiring manual intervention to scale the underlying infrastructure up or down.

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It increases the throughput of the model by optimizing memory allocation during inference.

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It ensures that the model always stays warm to minimize latency for every user request.

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It enables high availability by automatically replicating the model across multiple availability zones.

Question 4

Refer to the exhibit. The user is a member of the 'finance_team'. Why might the user encounter an access error when executing this join query?

Accepted Answer

The user lacks the USAGE privilege on the 'sales' schema.. In Unity Catalog, users require the USAGE privilege on all parent objects—the catalog and the schema—to access a table. While the user has USAGE on the catalog and SELECT on the tables, they lack the USAGE privilege on the 'sales' schema. Without explicit USAGE permission on the schema, the user cannot traverse the hierarchy to reference the tables, causing a permission denied error.

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The user lacks the SELECT privilege on the schema object.

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The user requires the MODIFY privilege to perform joins.

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The user needs to be an owner of the tables to perform joins.

Question 5

Refer to the exhibit. Why did the analyst group lose access after the table was recreated?

Accepted Answer

The new table is a different object, so the GRANT statement must be repeated.. In Unity Catalog, privileges are bound to the specific object ID. When a table is dropped and re-created, it becomes a new object with a new unique identifier. The previous GRANT statements, which were associated with the original object ID, do not automatically apply to the new table. This mechanism protects against security drift by ensuring that every new object must have its permissions explicitly managed.

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The catalog owner needs to refresh the table metadata.

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The analyst group needs to be re-added to the schema permissions.

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The user who recreated the table is not the catalog owner.

Question 6

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 7

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?

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 8

Which TWO of the following are benefits of using Databricks Asset Bundles for deploying AI applications?

Accepted Answer

Enables automated testing through CI/CD pipeline integration. Databricks Asset Bundles standardize the deployment process, making it repeatable, version-controlled, and easier to integrate into CI/CD pipelines. This reduces the risk of configuration drift, where the production environment deviates from the development environment due to manual changes in the UI. By treating infrastructure as code, teams can maintain a clear history of changes and improve the reliability and auditability of their AI application releases.

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Provides a graphical drag-and-drop interface for deployment

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Automatically handles data labeling and cleaning tasks

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Allows users to bypass Unity Catalog governance

Question 9

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 10

When designing a production-ready Databricks notebook for model inference, which TWO practices improve maintainability and performance?

Accepted Answer

Use Databricks Widgets for parameterizing inputs like model paths.. Modular code design allows for easier testing and unit-level debugging, which is crucial for complex inference logic. Using parameterization enables the notebook to be reused across different environments and models without changing the core code. These practices reduce technical debt and simplify the lifecycle of ML models, making the transition from development to production much smoother and ensuring consistent performance across different stages of the CI/CD pipeline.

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Embed all logic including data preprocessing in a single, large notebook.

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Hardcode all file paths and configuration settings for consistency.

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Always run inference on the driver node to avoid network latency.