Databricks-ML-Pro Model Deployment — All Questions With Answers

A machine learning engineer needs to deploy a custom MLflow model that requires a non-standard Python package not available in the default Databricks ML runtime. Which approach ensures the custom dependency is correctly packaged and loaded during model inference?

Question 2mediummultiple choice

An ML engineer needs to schedule daily batch inference for a fraud detection model processing millions of transactions stored in a Delta table. Which deployment pattern provides the most scalable and fault-tolerant architecture within Databricks?

Question 3mediummultiple choice

An ML engineer needs to deploy a custom PyTorch model to Databricks Model Serving with a custom preprocessing step that requires external Python packages. Which approach ensures the dependencies and custom logic execute correctly in the serverless inference container?

Question 4mediummultiple choice

A data engineering team is deploying a model using Databricks Model Serving. They need to ensure that the endpoint can handle sudden bursts of traffic while minimizing costs during periods of inactivity. Which configuration strategy for the 'workload_type' and 'scale_to_zero' parameters best achieves this balance?

Question 5hardmulti select

A company is using Databricks Feature Store to serve features for real-time inference. Which TWO components are required to ensure that the model can retrieve the latest feature values with sub-second latency? (Select TWO)

Question 6mediummultiple choice

An organization wants to implement a 'Shadow Deployment' for a new fraud detection model. How is this typically achieved in Databricks Model Serving, and what is its primary benefit?

Question 7mediummultiple choice

What happens when a model serving endpoint is configured with a 'Small' workload type but the model's Python environment requires 16GB of RAM to load the weights?

Question 8hardmulti select

A data science team is transitioning from batch prediction to real-time serving using MLflow. They need to ensure that the deployment process is reproducible and allows for easy rollback. Which TWO actions should the team take to meet these requirements?

Question 9mediummultiple choice

A machine learning engineer has registered a model in the Unity Catalog Model Registry and wants to serve it with Databricks Model Serving. The model's requirements.txt includes a private Python package hosted in an internal PyPI repository that requires authentication. Which configuration step is necessary to ensure the serving endpoint can install the package?

Question 10hardmultiple choice

An ML engineer is deploying a model to a Databricks Model Serving endpoint and needs to ensure that only authenticated users with specific permissions can invoke the endpoint. Which mechanism should they use?

Question 11mediummulti select

A machine learning engineer is deploying a model to Databricks Model Serving and needs to ensure that the endpoint can automatically scale based on traffic. The engineer wants to configure autoscaling to handle variable load. Which TWO actions should the engineer take? (Choose two.)

Question 12hardmulti select

An ML engineer is deploying a model to Databricks Model Serving and needs to ensure the endpoint can access sensitive data stored in Unity Catalog during inference. The model requires reading a feature table at request time. Which TWO configurations are necessary to enable this securely? (Choose two.)

Question 13easymultiple choice

A data scientist has registered a model in Unity Catalog and wants to deploy it to a Databricks Model Serving endpoint. They need to ensure that only the latest version of the model is served and that the endpoint automatically updates when a new version is registered. Which configuration should they use?

Question 14mediummultiple choice

An ML engineer is deploying a scikit-learn model to Databricks Model Serving. The model must be served on a GPU-enabled workload to meet latency requirements. Which combination of configuration settings should be used when creating the serving endpoint?

Question 15easymultiple choice

A data scientist has registered a model in Unity Catalog and wants to serve it in real-time. They need to ensure that the endpoint can be updated without downtime when a new model version is ready. Which deployment mode should they use?

Question 16mediummultiple choice

An ML engineer has registered a model in Unity Catalog with the name `prod.ml_models.fraud_detector`. The model has version 3 tagged as `champion` via the alias `champion`. The engineer now needs to update the serving endpoint `fraud-endpoint` to serve this specific model version using the alias. Which configuration correctly sets the served entity?

Question 17hardmulti select

A team is deploying a model to Databricks Model Serving. They want to ensure the endpoint can handle sudden spikes in traffic without dropping requests and that the model's dependencies are correctly resolved. Which TWO actions should they take? (Choose two.)

Question 18mediummultiple choice

An ML engineer is deploying a model to Databricks Model Serving and wants to ensure that the endpoint automatically scales down to zero when there is no traffic, thereby reducing cost. Which configuration should be applied to the endpoint?

Question 19hardmultiple choice

A team is deploying a PyTorch model to Databricks Model Serving. The model requires a custom Python package that is not available in the default serving environment. The team has uploaded the package as a wheel file to Unity Catalog volume at `/Volumes/prod/ml_artifacts/wheels/custom_pkg-0.1.0-py3-none-any.whl`. They need to include this dependency when logging the model. Which approach correctly ensures the wheel is installed in the serving environment?

Question 20hardmulti select

A team is using Databricks Model Serving to deploy a model that must handle sensitive data. They need to ensure that all inference requests and responses are logged for auditing purposes without exposing the data to unauthorized users. Which TWO configurations should they implement? (Choose two.)

Question 21mediummultiple choice

An ML engineer is deploying a model to Databricks Model Serving. The model's MLflow signature includes a pandas DataFrame input with 50 float features. To reduce inference latency, the engineer wants to enable request batching. Which configuration is required?

Question 22mediummultiple choice

A team is deploying a model to Databricks Model Serving and needs to ensure that the endpoint can handle sudden spikes in traffic. They configure autoscaling but notice that during spikes, the latency increases significantly before the endpoint scales up. What should they do to improve responsiveness?

Question 23mediummultiple choice

An ML engineer has registered a model in Unity Catalog as `prod.ml.forecast_model` and wants to serve it with Databricks Model Serving. The model's MLflow flavor is `mlflow.pyfunc` and it requires no custom dependencies. Which of the following is the correct way to create a serving endpoint for this model?

Question 24mediummultiple choice

A fraud detection model is registered in Unity Catalog as `prod.ml.fraud_model`. The model's training script uses scikit-learn version 1.3.2. An ML engineer creates a Model Serving endpoint and specifies the model version. During the first inference request, the endpoint fails with a `ModuleNotFoundError` for `scikit-learn==1.3.2`. The engineer confirms the model was logged with `mlflow.sklearn.log_model`. What is the most likely cause of this failure?

Question 25mediummultiple choice

An ML engineer has registered a scikit-learn model in the Unity Catalog Model Registry as `main.default.sales_forecast`. The model was logged with an MLflow signature and a conda environment. The engineer wants to deploy this model to Databricks Model Serving for real-time inference. Which step must be completed before creating the serving endpoint?

Question 26hardmultiple choice

An ML engineer is deploying a model to Databricks Model Serving that requires a specific version of a Python library that conflicts with the version in the default Databricks Runtime. They have logged the model with a conda environment that specifies the correct version. However, during deployment, the endpoint fails to start, and logs show a dependency conflict. What is the most likely cause?

Question 27mediummulti select

An ML engineer is configuring a Databricks Model Serving endpoint to serve a model that requires GPU acceleration. The endpoint must automatically scale based on traffic. Which TWO configurations are required to enable GPU serving and autoscaling? (Choose two.)

Question 28mediummulti select

A team is deploying a model to Databricks Model Serving that requires a custom Python library. The library is not available in the default environment. They need to ensure the endpoint can use this library. Which TWO methods can they use to include the custom library? (Choose two.)

Question 29mediummultiple choice

An ML engineer has registered a model in Unity Catalog with the name `prod.ml.forecast_model`. The model version 3 has been validated and approved for production. The engineer needs to make this version available for real-time inference through Databricks Model Serving, but the serving endpoint must automatically pick up any future version that is tagged as 'approved'. Which approach should the engineer use?

Question 30hardmulti select

An ML engineer is deploying a model to Databricks Model Serving and needs to enable authentication and authorization for the endpoint. Which TWO methods can be used to secure access to the endpoint? (Choose two.)

Question 31mediummultiple choice

An ML engineer is deploying a model to Databricks Model Serving that requires a custom Python package. The package is not available on PyPI but is stored as a wheel file in Unity Catalog volumes. What is the correct way to include this package in the model's environment?

Question 32hardmulti select

A fraud detection team is deploying a new model to Databricks Model Serving. They want to route a small percentage of live traffic to the new model while keeping the existing model as the primary, and they need to monitor both models' performance. Which TWO configurations are required to achieve this? (Choose two.)

Question 33hardmultiple choice

An ML engineer is deploying a model to Databricks Model Serving that requires a custom Python library. The library is not available in the default serving environment. The engineer has packaged the library into a wheel file and stored it in Unity Catalog Volumes. They want to ensure the library is available when the model is served. Which approach should they use to include the custom library?

Question 34mediummulti select

A machine learning engineer is deploying a model to Databricks Model Serving that requires a custom Python library. The library is not available in the default Databricks Runtime. The engineer needs to ensure the library is available at inference time. Which TWO methods can be used to include the custom library? (Choose two.)

Question 35easymultiple choice

A data scientist has registered a model in Unity Catalog and wants to deploy it to Databricks Model Serving. The model version is currently in 'Staging' stage. What must the data scientist do before the model can be served?

Question 36hardmulti select

A team is deploying a model to Databricks Model Serving and wants to implement A/B testing between two model versions. They need to route a percentage of traffic to each version and collect metrics. Which TWO configurations are required to achieve this? (Choose two.)

Question 37easymultiple choice

A machine learning engineer is deploying a model to Databricks Model Serving and wants to ensure that the endpoint automatically scales down to zero when there is no traffic, to minimize costs. Which configuration setting should they enable?

Question 38hardmultiple choice

An ML engineer is deploying a model to Databricks Model Serving that will be used for real-time predictions. The model's inference function has a high memory footprint and requires a significant amount of RAM. The engineer configures the endpoint with a 'Large' workload type, which provides 8 GB of memory. However, during testing, the endpoint frequently runs out of memory and returns errors. The engineer needs to resolve this issue while minimizing cost. What should the engineer do?

Question 39hardmultiple choice

A team is using Databricks Model Serving to serve a model that requires a custom Python library. They have packaged the library as a wheel file and uploaded it to Unity Catalog volumes. How can they ensure the library is installed when the model is served?

Question 40hardmultiple choice

An ML engineer needs a serving endpoint that routes traffic between two model versions, sending 90 percent of requests to version 3 and 10 percent to version 4, while guaranteeing that any single client's consecutive requests during a session land on the same version. Which Databricks Model Serving capability should be used?

Question 41hardmultiple choice

A team deploys a model to a Databricks Model Serving endpoint and enables Inference Tables. They later notice that requests are being served successfully but no rows are appearing in the inference table. Which explanation is most likely?

Question 42mediummultiple choice

A machine learning engineer needs to deploy a custom scikit-learn model to a Databricks Model Serving endpoint with a strict response time SLA of under 50 milliseconds. The model includes an extensive text-cleaning pipeline that utilizes heavy regex matching. How should the engineer package the model to ensure maximum inference efficiency and meet the low-latency requirement?

Question 43mediummultiple choice

A machine learning engineer needs to deploy a custom PyTorch model to a Databricks Model Serving endpoint. The model requires a custom pre-processing step that is not part of the standard MLflow transformers or pyfunc flavor. Which deployment approach ensures the custom logic executes reliably within the serverless serving container?

Question 44hardmulti select

A data science team is preparing to deploy a high-throughput recommendation model using Databricks Model Serving. Which TWO factors must be considered to optimize endpoint latency and resource utilization? (Choose two)

Question 45mediummultiple choice

A machine learning engineer needs to deploy a custom PyTorch model to a Databricks Model Serving endpoint. The model requires custom post-processing logic and loading auxiliary tokenizer files alongside the serialized weights. Which approach provides the correct mechanism to package and serve this custom artifact?

Question 46mediummultiple choice

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?

Question 47mediummulti select

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)

Question 48hardmultiple choice

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?

Exhibit

{
  "served_entities": [
    {
      "name": "churn-model-v1",
      "entity_name": "prod.ml_models.churn_prediction",
      "entity_version": "1",
      "workload_size": "Small",
      "scale_to_zero_enabled": true,
      "traffic_config": {
        "percent": 90
      }
    },
    {
      "name": "churn-model-v2",
      "entity_name": "prod.ml_models.churn_prediction",
      "entity_version": "2",
      "workload_size": "Small",
      "scale_to_zero_enabled": true,
      "traffic_config": {
        "percent": 10
      }
    }
  ]
}

Question 49mediummultiple choice

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?

Question 50easymultiple choice

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

Question 51mediummultiple choice

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?

Question 52hardmulti select

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)

Question 53mediummultiple choice

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

Question 54hardmultiple choice

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?

Exhibit

GET /serving-endpoints/customer-churn/config
{
  "name": "customer-churn",
  "config": {
    "served_entities": [
      {
        "entity_name": "prod.models.churn",
        "entity_version": "5",
        "scale_to_zero_enabled": false
      }
    ]
  }
}

Question 55mediummulti select

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)

Question 56hardmultiple choice

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?

Question 57hardmultiple choice

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?

Exhibit

{
  "name": "churn-model-endpoint",
  "config": {
    "served_models": [
      {
        "model_name": "churn-model",
        "model_version": "5",
        "workload_size": "Small",
        "scale_to_zero_enabled": true
      }
    ]
  }
}

Question 58mediummultiple choice

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?

Question 59mediummultiple choice

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?

Question 60hardmultiple choice

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?

Question 61mediummulti select

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.)

Question 62easymultiple choice

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?

Question 63hardmultiple choice

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?

Question 64hardmultiple choice

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?

Question 65hardmultiple choice

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?

Question 66easymultiple choice

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?

Question 67mediummultiple choice

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?

Question 68hardmultiple choice

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?

Question 69mediummulti select

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.)

Question 70mediummultiple choice

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?

Question 71hardmultiple choice

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?

Question 72hardmultiple choice

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?

Question 73hardmultiple choice

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?

Question 74hardmultiple choice

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?

Question 75easymultiple choice

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?

Question 76mediummultiple choice

An ML engineer is updating a model serving endpoint to use a new model version. They want to gradually shift traffic from the old version to the new version to monitor performance before full rollout. Which feature of Databricks Model Serving should they use?

Question 77mediummultiple choice

A data scientist is deploying a model to Databricks Model Serving. The model was trained using a scikit-learn pipeline that includes a custom transformer. The custom transformer is defined in a Python module that is not part of the model artifact. What should the data scientist do to ensure the model can be served successfully?

Question 78hardmultiple choice

A team has deployed a model to Databricks Model Serving and enabled inference tables. They notice that the inference table contains request and response payloads but no ground truth labels. They want to automatically join ground truth labels for monitoring. What should they do?

Question 79mediummultiple choice

An ML engineer has registered a scikit-learn model in the Unity Catalog Model Registry and wants to serve it as a real-time endpoint using Databricks Model Serving. The model's MLflow signature expects a JSON payload with an array of records. The engineer creates a serving endpoint with a workload size of Medium and configures the served entity to use the latest model version. Which additional configuration is required to enable automatic payload logging to a Delta table for monitoring?

Question 80easymultiple choice

A data scientist has deployed a model to a Databricks Model Serving endpoint. The endpoint is configured with scale-to-zero enabled and a workload size of Small. After a period of inactivity, the endpoint scales down to zero. A client application sends a request to the endpoint after this idle period. What happens to the first request?

Question 81easymultiple choice

A data scientist has trained a model and registered it in Unity Catalog. They now need to deploy it for real-time inference with automatic scaling and a REST API endpoint. Which Databricks feature should they use?

Question 82hardmultiple choice

A company has deployed a model to a Databricks Model Serving endpoint. The model's predictions must be logged to a Delta table for monitoring and auditing. The ML engineer wants to enable inference logging without modifying the model's code. Which approach achieves this with minimal effort?

Question 83hardmulti select

An ML engineer is deploying a model to Databricks Model Serving and needs to enable automatic scaling based on traffic. The model has variable inference latency and the team wants to optimize cost while maintaining performance. Which TWO configurations are required to achieve this? (Choose two.)

Question 84mediummultiple choice

An ML engineer is deploying a scikit-learn model to a Databricks Model Serving endpoint. The model expects a single feature vector of 10 float values per request. The endpoint must return predictions in under 100 ms. Which approach should the engineer use to minimize per-request overhead?

Question 85hardmultiple choice

An ML engineer is deploying a model to Databricks Model Serving that uses a custom Python function as a pre-processing step. The function relies on a global variable defined in a separate module. After deployment, the endpoint returns errors indicating the global variable is not defined. The engineer confirmed the module is included in the model's conda environment. What is the most likely cause?

Question 86mediummultiple choice

A data scientist has deployed a model to Databricks Model Serving and wants to monitor its performance over time. They need to track prediction drift and data quality issues. Which Databricks feature should they use to automatically capture inference logs and compute metrics?

Question 87hardmultiple choice

A fraud detection model is deployed to a Databricks Model Serving endpoint. The team wants to test a new model version without affecting existing predictions. They need to send a copy of live traffic to the new version and log its predictions for comparison, while the current version continues to serve all responses. Which feature should they use?

Question 88mediummultiple choice

A data science team is using Databricks Model Serving to deploy a model that must process sensitive data. They need to ensure that all inference requests are logged for auditing purposes, including the input data and predictions. Which approach should they take?

Question 89easymultiple choice

A team has deployed a model to a Databricks Model Serving endpoint. They want to monitor the endpoint's performance and detect data drift over time. Which Databricks feature should they use to automatically track inference data and compute drift metrics?

Question 90hardmultiple choice

An ML engineer is updating a production model serving endpoint to use a new model version. The endpoint currently serves version 1 with the 'Champion' alias. The engineer wants to test version 2 with a small percentage of live traffic before full rollout. Which deployment strategy should they use in Databricks Model Serving?

Question 91mediummulti select

An ML engineer is deploying a model to Databricks Model Serving and needs to ensure that the endpoint can handle traffic spikes while minimizing costs during idle periods. The engineer considers enabling scale-to-zero and configuring autoscaling. Which TWO statements about these features are correct? (Choose two.)

Question 92mediummultiple choice

An ML engineer is deploying a model to Databricks Model Serving that requires a custom Python package not available in the default environment. The model was logged with MLflow and includes the package in its conda environment. What must the engineer ensure for the endpoint to successfully load the model?

Question 93easymultiple choice

An ML engineer needs to deploy a model to Databricks Model Serving that requires a specific version of a Python library. The library is available on PyPI. Where should the engineer specify this dependency?

Question 94easymultiple choice

A data science team has deployed a model to Databricks Model Serving and wants to ensure that the endpoint can handle sudden spikes in traffic without manual intervention. Which feature should they configure?

Question 95hardmulti select

An ML engineer is deploying a model to Databricks Model Serving and needs to ensure the endpoint can handle sudden spikes in traffic without downtime. The model has a large memory footprint and takes several seconds to load. Which TWO configurations should the engineer implement to achieve this? (Choose two.)

Question 96easymultiple choice

A data scientist has registered a model in Unity Catalog and wants to deploy it to a Databricks Model Serving endpoint. The model version is 3 and the model name is 'fraud_model'. Which identifier should be used to reference this model version when creating the endpoint?

Question 97hardmultiple choice

A machine learning engineer is deploying a model to Databricks Model Serving and wants to implement a blue-green deployment strategy. They have registered two model versions in Unity Catalog: version 1 (current production) and version 2 (new candidate). They want to route 10% of traffic to version 2 for testing while keeping 90% on version 1. Which feature should they use to achieve this?

Question 98mediummultiple choice

An ML engineer has deployed a model to Databricks Model Serving and wants to update the endpoint to serve a new model version without changing the endpoint URL or causing downtime. Which approach is correct?

Question 99mediummultiple choice

A fraud detection team has a model registered in Unity Catalog as main.ml.fraud_model. They need to serve it in real time, but compliance requires that every scoring request automatically generate an audit record in a Delta table, and that the model only be promoted to production after a human reviews the audit logs from a canary period. Which deployment configuration satisfies these requirements?

Question 100mediummulti select

A team is deploying a scikit-learn model to Databricks Model Serving and wants to minimize cold-start latency so that the first request after a period of inactivity is still fast. Which TWO actions help achieve this? (Choose two.)

Question 101easymultiple choice

A data scientist has a model registered in Unity Catalog and wants to let an external application score it over HTTPS without embedding Databricks credentials in the application. The application's identity is already a service principal in the workspace. Which approach should be used to authenticate calls to the Model Serving endpoint?

Question 102hardmultiple choice