Databricks-ML-Pro Model Deployment Practice Test 4 — 15 Questions

Question 1

A machine learning engineer needs to deploy a custom scikit-learn model to a Databricks Model Serving endpoint with maximum throughput and minimum latency. The model requires an external preprocessing Python script during inference. Which deployment approach best leverages MLflow and Databricks architecture?

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

Accepted Answer

Log the model and preprocessing code using custom pyfunc, register it in Unity Catalog, and configure a serverless Model Serving endpoint.. Packaging the custom preprocessing logic along with the scikit-learn estimator into an MLflow PyFunc model ensures that all inference data transformations happen inside the serving container natively. This prevents client-side processing bottlenecks and guarantees consistent feature engineering between training and real-time serving environments.

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Deploy the raw scikit-learn model artifact to Model Serving and execute the preprocessing logic inside a separate Spark structured streaming job.

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Write the preprocessing code inside a Databricks notebook and call the model endpoint using a client-side REST API request.

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Save the model weights to DBFS and build an external Flask application on Amazon EC2 to manage traffic routing and inference.

Question 2

An ML engineer is transitioning a model from the Workspace Model Registry to the Unity Catalog (UC) Model Registry. Which TWO statements describe benefits or requirements of using Unity Catalog for model management? (Select TWO)

Accepted Answer

Unity Catalog supports model sharing across multiple Databricks workspaces.. Unity Catalog centralizes governance for all data and AI assets, providing a unified interface for access control and lineage. Transitioning to UC enables fine-grained permissions and allows models to be shared across different workspaces within the same Metastore. This is a significant improvement over the legacy workspace-specific registry which lacked cross-workspace visibility and unified auditing.

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Models must be stored in a legacy DBFS location to be registered in Unity Catalog.

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The legacy MLflow Stages (Staging, Production) are the primary way to manage UC models.

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Only models logged with the SparkML flavor can be registered in the Unity Catalog.

Question 3

Refer to the exhibit. A machine learning team has updated their model serving endpoint configuration as shown in the JSON. Which deployment strategy is being implemented, and what is the primary risk associated with this specific configuration?

Accepted Answer

Canary deployment; the primary risk is increased 'cold start' latency for the v2 model due to low traffic volume.. The exhibit demonstrates a Canary deployment where a small fraction of traffic (10%) is routed to a new model version (v2) while the majority remains on the stable version (v1). This allows for real-world testing with minimal impact. However, since both versions are set to scale-to-zero, the 10% traffic might not be frequent enough to keep v2 warm, leading to high latency for those users.

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Blue/Green deployment; the primary risk is the high cost of running two identical clusters simultaneously.

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A/B testing; the primary risk is that the workload size 'Small' is insufficient for 90% of the traffic.

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Shadow deployment; the primary risk is that v2 will interfere with the predictions returned by v1.

Question 4

A data scientist needs to perform batch inference on a large dataset stored in Delta Lake using a model registered in the Unity Catalog. Which approach is most efficient for leveraging Spark's distributed computing capabilities while using the MLflow model?

Accepted Answer

Using mlflow.pyfunc.spark_udf to wrap the model and applying it to the Spark DataFrame columns.. MLflow provides a built-in function to load models as Spark User Defined Functions (UDFs). This allows the model to be distributed across the executor nodes of a Spark cluster, enabling parallel processing of large datasets. This method is preferred over manual iteration as it integrates seamlessly with the Spark DataFrame API and optimizes resource utilization during high-volume batch jobs.

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Loading the model with mlflow.pyfunc.load_model and using a for-loop to iterate over DataFrame rows.

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Converting the Delta table to a Pandas DataFrame and using the standard model.predict() method.

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Calling the Model Serving REST API for every row in the Delta Lake table using a standard Python request.

Question 5

When deploying a model to a Databricks Model Serving endpoint, what is the purpose of the 'Small', 'Medium', and 'Large' workload size settings?

Accepted Answer

They specify the amount of CPU and memory allocated to each model instance.. The workload size setting in Databricks Model Serving defines the compute resources (CPU and Memory) allocated to each instance of the model. Choosing the right size is a trade-off between the complexity of the model's computation and the cost of the infrastructure. Larger models or those with heavy preprocessing requirements need more resources to maintain low latency.

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They define the maximum number of concurrent requests the endpoint can handle.

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They determine the geographic region where the model will be hosted.

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They select the version of the MLflow library used for deployment.

Question 6

An ML engineer wants to ensure that only models that have passed a specific validation suite can be assigned the 'Champion' alias in Unity Catalog. What is the recommended way to automate this process?

Accepted Answer

Using a Databricks Workflow to run validation and then calling the MLflow Client to update the alias.. Automation in the model lifecycle is best achieved using Databricks Workflows or CI/CD pipelines. By creating a task that runs a validation notebook, the system can programmatically update model aliases via the MLflow Client API only after all tests pass. This ensures a consistent, governed promotion process that prevents low-quality models from reaching production.

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Manually checking the validation results and updating the alias in the UI.

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Setting a SQL trigger on the Model Registry table to update the alias automatically.

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Configuring the Model Serving endpoint to automatically promote the newest version.

Question 7

A team is deploying a model that requires custom Python libraries not available in the default Databricks Runtime. Which TWO methods can be used to ensure these dependencies are available in the Model Serving environment? (Select TWO)

Accepted Answer

Include a 'requirements.txt' file or a 'conda.yaml' when calling mlflow.log_model().. Model Serving environments are reconstructed based on the metadata captured when the model was logged. To include custom libraries, they must be explicitly defined during the logging process. This ensures that the production environment exactly matches the development environment, preventing 'missing module' errors during real-time inference requests.

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Manually install the libraries on the driver node of the cluster used for training.

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Add the libraries to the Spark configuration of the Model Serving endpoint.

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Upload the wheel files to a DBFS location and reference them in the endpoint UI.

Question 8

Which statement best describes the role of the 'Signature' in an MLflow model when deploying to a Databricks Model Serving endpoint?

Accepted Answer

It specifies the data types and names of the input features and the output predictions.. An MLflow Model Signature defines the schema of the inputs and outputs for a model. This is crucial for Model Serving as it allows the endpoint to perform input validation before the data reaches the model. It also enables the UI to generate a 'Call' template, making it easier for developers to test the API.

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It is a cryptographic hash used to verify the integrity of the model files.

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It lists the specific users and groups who have permission to call the endpoint.

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It contains the hyperparameters used during the final training run of the model version.

Question 9

Refer to the exhibit. An administrator notices that the cost for this specific endpoint is higher than expected even when there is no traffic. Based on the exhibit, what is the most likely cause of the high idle cost?

Accepted Answer

The 'scale_to_zero_enabled' parameter is set to false, keeping an instance active at all times.. The configuration shows that 'scale_to_zero_enabled' is set to false. This means that at least one instance of the model is running 24/7, regardless of whether any requests are being made. While this ensures there is never a cold start, it leads to continuous billing for the compute resources even during nights and weekends.

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The 'entity_version' is set to a legacy version that uses more expensive hardware.

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The endpoint is using a GPU-accelerated instance by default for all Unity Catalog models.

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Multiple versions of the model are being served simultaneously, doubling the cost.

Question 10

When using the Unity Catalog Model Registry, what are the primary advantages of using 'Aliases' over 'Versions' when calling a model from a production application? (Select TWO)

Accepted Answer

Aliases allow the application to always point to a stable name like '@prod' instead of a hardcoded version number.. Aliases provide a layer of abstraction between the application code and the specific model version. This decoupling is a cornerstone of MLOps, as it allows for seamless updates and rollbacks without modifying the client-side code that consumes the model. It also improves readability by using descriptive names like '@champion'.

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Aliases improve performance by caching the model weights on the client side.

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Aliases are required to enable GPU acceleration for models in Unity Catalog.

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Only models with an alias can be used in Spark structured streaming jobs.

Question 11

An ML engineer is deploying a model that includes a custom Python class for preprocessing. During deployment to a Model Serving endpoint, the model fails to load with a 'ModuleNotFoundError'. What is the most likely cause of this error despite having the class in the training notebook?

Accepted Answer

The custom class was not saved as a separate .py file and included in the 'code_paths' parameter during logging.. When MLflow logs a model, it captures the environment but not necessarily the local code or classes defined in the notebook unless they are part of a package or provided as a code dependency. For custom classes to be available in the serving container, they must be included in the 'code_paths' argument of the log_model function.

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The Model Serving endpoint does not support custom Python classes for security reasons.

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The data scientist forgot to install the 'databricks-model-serving' library in the training cluster.

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The custom class must be registered in the Unity Catalog as a separate 'Function' entity.

Question 12

Refer to the exhibit. The JSON configuration represents an existing Databricks Model Serving endpoint. You need to update this endpoint to support a traffic split between version 5 and version 6 for A/B testing. Which update strategy is correct?

Accepted Answer

Modify the served_models array to include both versions with specific traffic weights assigned to each.. To perform A/B testing, you must define multiple served models within the `served_models` array in the endpoint configuration. Each entry requires a `traffic` weight that sums to 100%. This allows Databricks to route incoming requests according to the specified percentages. This method is crucial for safely rolling out new models, as it allows you to observe performance metrics on a subset of real-world traffic before a full production cutover.

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Update the config to replace 'model_version': '5' with 'model_version': '6' in the current served_models list.

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Create two separate endpoints, one for version 5 and one for version 6, then split traffic using a load balancer.

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Delete the current endpoint and recreate it with a new configuration that includes only version 6.

Question 13

When deploying a model to Databricks Model Serving, you notice that inference latency is higher than expected. Which diagnostic approach is most effective for identifying the bottleneck?

Accepted Answer

Check the built-in request metrics and logs for the serving endpoint to analyze duration distribution.. Monitoring tools provided by Databricks, such as the built-in request metrics and logs, are essential for identifying latency bottlenecks. By examining request volume, processing duration, and resource utilization, you can determine if the latency is due to model complexity, infrastructure constraints, or external data dependencies. This allows for data-driven optimization, such as choosing a larger workload size, optimizing the model architecture, or implementing caching for frequently accessed data inputs.

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Re-train the model with a smaller dataset to see if it improves performance.

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Increase the number of instances in the model serving endpoint without checking logs.

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Restart the Databricks workspace to clear cached inference results.

Question 14

An ML engineer is deploying a scikit-learn model to a Databricks Model Serving endpoint. The model's inference function requires access to an external feature store table for real-time feature lookup. Which approach allows the model to retrieve these features during serving while maintaining low latency and avoiding per-request authentication complexity?

Accepted Answer

Use the Databricks Feature Store's online table and call the feature lookup client within the model's predict method.. The Databricks Feature Store online table is purpose-built for low-latency feature serving. By embedding the feature lookup client in the model's predict method, the endpoint can retrieve features in real time without managing authentication. This integration is a core pattern for deploying models that rely on feature store data, ensuring consistency between training and serving.

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Package the feature store data as a static file within the model artifact and load it during initialization.

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Enable the model to query the Delta table directly using Spark during each inference request.

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Configure the serving endpoint to call an external REST API that returns feature values, using a personal access token stored in the environment.

Question 15

A financial services company uses Databricks Model Serving to deploy a real-time fraud detection model. The endpoint is configured with scale-to-zero enabled. During a period of no traffic, the endpoint scales down to zero. When a sudden burst of requests arrives, the first few requests experience high latency. Which mechanism is responsible for this behavior?

Accepted Answer

The endpoint is experiencing a cold start as it provisions resources and loads the model into memory.. Scale-to-zero reduces cost by scaling down to zero replicas when idle. When traffic resumes, the endpoint must cold start: provision compute, pull the image, and load the model. This causes latency for the first few requests. The other options describe issues that would persist regardless of scaling, such as concurrency limits or per-request builds.

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The requests are being queued because the endpoint's maximum concurrency is set too low.

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The model's dependencies are being downloaded from PyPI on each request.

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The model's container image is being rebuilt from scratch for each request.