Databricks-ML-Pro Model Deployment Practice Test 5 — 15 Questions

Question 1

A data science team is deploying a model to Databricks Model Serving and needs to ensure that the endpoint can handle sudden spikes in traffic without dropping requests. They want to configure auto-scaling appropriately. Which TWO parameters should they adjust to control the scaling behavior? (Choose two.)

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

Accepted Answer

Set the minimum number of replicas to a value greater than zero.. Auto-scaling in Databricks Model Serving is governed by the minimum and maximum replica counts. The minimum ensures baseline capacity to handle initial bursts, while the maximum allows the endpoint to scale out to meet high demand. These two parameters directly control the scaling range and are essential for handling traffic spikes without dropping requests.

Answer

Enable scale-to-zero to reduce costs during idle periods.

Answer

Adjust the model's batch size in the predict function to process more requests per inference.

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Set the endpoint's workload size to 'Large' to increase per-replica capacity.

Question 2

An ML engineer has deployed a model to Databricks Model Serving and wants to monitor the endpoint's performance over time. They need to track the number of requests, latency, and error rates. Which Databricks feature provides these metrics out-of-the-box?

Accepted Answer

Databricks Model Serving endpoint metrics in the Databricks UI. Databricks Model Serving includes built-in metrics that are accessible in the Databricks UI. These metrics cover request counts, latency, and error rates, providing immediate visibility into endpoint performance. Other options like MLflow Tracking or Unity Catalog audit logs serve different purposes and do not offer out-of-the-box endpoint monitoring.

Answer

Delta Live Tables

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Unity Catalog audit logs

Answer

MLflow Tracking

Question 3

An ML engineer is deploying a model to Databricks Model Serving and wants to implement A/B testing between two model versions. The engineer needs to route a percentage of traffic to each version and collect performance metrics. Which feature of Databricks Model Serving should the engineer use?

Accepted Answer

Create a single endpoint with multiple model versions and configure traffic splitting between them.. To perform A/B testing between model versions, the engineer should create a single Model Serving endpoint that serves multiple model versions with traffic splitting. This allows a specified percentage of requests to be routed to each version, and Databricks provides metrics for each version, facilitating performance comparison. This native feature simplifies A/B testing.

Answer

Deploy two separate endpoints and use an external load balancer to distribute traffic between them.

Answer

Enable the 'Canary' deployment option in the endpoint configuration to automatically split traffic.

Answer

Use MLflow's model registry to assign a stage to each model version and route traffic based on the stage.

Question 4

A team is deploying a model to Databricks Model Serving that requires a specific version of a Python library that conflicts with the version pre-installed in the serving environment. They include the library version in the model's requirements.txt. However, upon deployment, the endpoint fails to start, and logs indicate a dependency conflict. What is the most likely cause of this failure?

Accepted Answer

The library version conflicts with a pre-installed library that is required by the serving infrastructure, causing a dependency resolution failure.. Model Serving environments come with pre-installed libraries that support the serving infrastructure. If a model's requirements.txt specifies a version that conflicts with these, the dependency resolver may fail, preventing the endpoint from starting. The solution is to align the requested version with the pre-installed one or use a custom container image to isolate dependencies.

Answer

The model's requirements.txt is not being parsed correctly due to a syntax error.

Answer

The serving environment ignores requirements.txt and uses only the pre-installed libraries.

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The specified library version is incompatible with the Python version used by the serving environment.

Question 5

An ML engineer is deploying a model to Databricks Model Serving that requires a custom Python package. The package is not available in the default environment and must be installed from a private PyPI repository. Which method ensures the package is available to the model at serving time?

Accepted Answer

Specify the package's index URL and credentials in the model's conda environment file, and ensure the serving endpoint has network access to the private repository.. To use a private PyPI repository, the model's conda environment must include the index URL and credentials. Databricks Model Serving builds the environment based on this specification, provided it has network access to the repository. This ensures the custom package is installed and available during inference.

Answer

Add the package to the cluster's init script and ensure the cluster is attached to the serving endpoint.

Answer

Include the package as a wheel file in the model's artifact directory and reference it in the model's conda environment.

Answer

Upload the package to DBFS and use a %pip install command in a notebook before deploying the model.

Question 6

A data scientist has registered a model in Unity Catalog and wants to deploy it to a Databricks Model Serving endpoint. What is the simplest way to create the endpoint?

Accepted Answer

Use the Databricks UI to create a new serving endpoint and select the registered model.. The Databricks UI offers a simple, integrated way to create a Model Serving endpoint by selecting a registered Unity Catalog model. It handles configuration and deployment automatically, making it the easiest method for data scientists.

Answer

Create a Databricks job that runs the model's predict method on a schedule.

Answer

Write a Python script using the MLflow library to deploy the model to a local server.

Answer

Export the model as a pickle file and upload it to a Databricks cluster.

Question 7

A machine learning engineer has a model registered in Unity Catalog as prod.ml.iris_model. They need to deploy it to a real-time serving endpoint that automatically scales based on traffic and provides a REST API for predictions. The model's signature is logged. Which deployment method should they use?

Accepted Answer

Use Databricks Model Serving to create a new endpoint and select the model from Unity Catalog, specifying the model version or alias.. Databricks Model Serving provides a fully managed, scalable solution for deploying models registered in Unity Catalog. It automatically creates a REST endpoint, handles scaling, and integrates with governance features. The other options either rely on manual infrastructure or are intended for development, not production real-time serving.

Answer

Use MLflow's built-in serving command to start a local REST server on a cluster and expose it via a public URL.

Answer

Create a Databricks job that runs a Python script to load the model and listen for HTTP requests on a driver node.

Answer

Export the model as a Docker image using MLflow and deploy it to a Kubernetes cluster managed outside Databricks.

Question 8

A team has deployed a model to Databricks Model Serving and wants to enable autoscaling to handle variable traffic. They configure the endpoint with scale_to_zero_enabled set to true and a min_provisioned_concurrency of 0. After deployment, they notice that the endpoint takes several seconds to respond to the first request after a period of inactivity. What is the cause of this latency?

Accepted Answer

The endpoint is scaling from zero, which requires provisioning resources and loading the model before serving the request.. Enabling scale_to_zero with zero minimum concurrency allows the endpoint to shut down completely when idle, reducing cost. However, the next request must wait for compute resources to be provisioned and the model to be loaded, resulting in cold-start latency. To avoid this, set a min_provisioned_concurrency greater than zero or disable scale_to_zero, keeping at least one replica warm.

Answer

The model is being reloaded from Unity Catalog on each request, causing cold-start latency.

Answer

The endpoint is using a GPU workload, and GPU initialization takes several seconds after each idle period.

Answer

The model's Python environment is being reinstalled on each request due to missing dependencies in the container image.

Question 9

A team is deploying a model to Databricks Model Serving and wants to implement a canary release strategy to gradually shift traffic from the current model version to a new version. Which TWO configurations are required to achieve this? (Choose two.)

Accepted Answer

Create a serving endpoint with multiple served entities, each referencing a different model version.. Canary releases in Databricks Model Serving are achieved by configuring a single endpoint with multiple served entities, each pointing to a different model version. Traffic is then split between these entities using the endpoint's traffic configuration, which can be updated via the REST API. This allows gradual shifts and monitoring.

Answer

Deploy two separate endpoints and use an external load balancer to distribute traffic between them.

Answer

Enable automatic canary deployment by setting a flag in the model's MLflow metadata.

Answer

Configure the endpoint to use a single served entity and rely on the model's internal logic to route requests based on a random seed.

Question 10

An ML engineer needs to deploy a model to Databricks Model Serving that requires a custom Python package. The package is not available in the default Databricks Runtime and must be installed from a private PyPI repository. Which approach should be used to include this package in the model's environment?

Accepted Answer

Include the package in the model's conda.yaml or requirements.txt file when logging the model with MLflow.. Databricks Model Serving builds a container for the model based on the environment specified during MLflow model logging. By including the private PyPI package in the conda.yaml or requirements.txt with the appropriate index URL, the package is installed in the serving environment. This is the supported method for custom dependencies.

Answer

Install the package on the driver node of the serving cluster using a %pip magic command before deployment.

Answer

Add the package to the cluster's init script and reference the cluster when creating the serving endpoint.

Answer

Upload the package as a Databricks notebook and import it at runtime within the model's predict function.

Question 11

An ML engineer is deploying a model to a Databricks Model Serving endpoint. The model's inference function logs predictions to a Delta table for monitoring. During testing, they notice that the logging adds significant latency. They need to reduce the impact on inference latency. Which approach should they take?

Accepted Answer

Move the logging to an asynchronous background process.. Asynchronous logging moves the logging operation outside the critical path of inference, allowing predictions to be returned without waiting for the log write to complete. This is the most effective way to reduce latency caused by logging in a serving endpoint. Databricks Model Serving supports asynchronous logging patterns, such as using the inference table or custom async code.

Answer

Enable auto-scaling to handle the additional load from logging.

Answer

Reduce the frequency of logging by sampling predictions.

Answer

Increase the workload size of the serving endpoint.

Question 12

An ML engineer is deploying a model to Databricks Model Serving that uses a custom transformer requiring a GPU. The endpoint must handle high throughput with low latency. Which workload type and configuration should be selected?

Accepted Answer

Use a 'GPU_SMALL' or 'GPU_MEDIUM' workload type, depending on the model's memory and compute needs.. Databricks Model Serving provides specific GPU-enabled workload types, such as 'GPU_SMALL' and 'GPU_MEDIUM', which include GPU instances. For a model requiring GPU, selecting one of these workload types is necessary. The choice between small and medium depends on the model's memory and compute demands to achieve high throughput and low latency.

Answer

Use a 'Large' workload type and specify GPU requirements in the model's conda environment.

Answer

Use a 'Medium' workload type and configure the endpoint to use GPU by setting an environment variable.

Answer

Use a 'Small' workload type with CPU and enable GPU acceleration via a model parameter.

Question 13

A machine learning engineer has deployed a model to a Databricks Model Serving endpoint. The model requires a custom Python package that is not available in the default environment. The engineer has already logged the model with MLflow and included the package in the conda environment. However, upon deployment, the endpoint fails to start. What is the most likely cause?

Accepted Answer

The custom package is not available in a public PyPI repository, and no additional index URL was specified.. When deploying a model with custom dependencies, Databricks Model Serving uses the conda environment logged with the MLflow model. If the package is not on PyPI or requires a private index, you must specify the index URL in the conda environment. Without it, the installation fails, and the endpoint cannot start. Ensuring the environment specification includes all necessary sources is critical for successful deployment.

Answer

The model's MLflow flavor is not supported by Model Serving.

Answer

The custom package is not installed on the cluster used for serving.

Answer

The endpoint's workload size is too small to install the package.

Question 14

A financial institution deploys a credit scoring model using Databricks Model Serving. The model must log all incoming requests and outgoing responses to a Delta table for auditing. The ML engineer needs to enable this logging with minimal performance impact. Which solution should they implement?

Accepted Answer

Enable inference logging on the serving endpoint, specifying a Delta table location for logs.. Databricks Model Serving includes a built-in inference logging feature that asynchronously logs request and response data to a Delta table. This is the most efficient and least intrusive method. Custom wrappers or external systems add latency and complexity. Access logs do not contain payloads. Therefore, enabling inference logging directly is the correct solution.

Answer

Implement a custom logging wrapper in the model's predict method that writes each request to a Delta table.

Answer

Use a Databricks job to periodically query the endpoint's access logs and write them to a Delta table.

Answer

Configure the model to send logs to an external Kafka topic, then use a Databricks job to ingest from Kafka into Delta Lake.

Question 15

An ML engineer needs to deploy a model to Databricks Model Serving. The model was logged with MLflow and registered in Unity Catalog. The engineer wants to ensure that only the latest version of the model is served and that the endpoint can be updated without downtime. Which approach should they use?

Accepted Answer

Update the existing endpoint to serve the new model version using the Databricks UI or REST API, which performs a rolling update.. Updating an existing endpoint to a new model version via the UI or REST API triggers a rolling update, ensuring zero downtime and keeping the same endpoint URL. This is the standard method for deploying a new version without disrupting clients.

Answer

Delete the existing endpoint and create a new one with the same name and configuration, pointing to the new model version.

Answer

Use MLflow's transition request to move the model to Production stage, which automatically updates the serving endpoint.

Answer

Create a new endpoint for each model version and update the client application to point to the new endpoint URL.