Databricks-ML-Assoc Model Deployment Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  It is always the cheapest option for low-traffic models.

  Why it's wrong here

  Provisioned throughput is usually more expensive than serverless options because it involves paying for dedicated reserved capacity. For low-traffic models, a serverless or auto-scaling endpoint is generally more cost-effective as it can scale down to zero when not in use, avoiding unnecessary costs for idle resources.

• ✓

  It provides guaranteed performance and lower latency for heavy workloads.

  Why this is correct

  Provisioned Throughput reserves dedicated resources for the model, which eliminates the variability associated with shared serverless infrastructure. This results in consistent, predictable performance and lower latency, making it the ideal choice for production applications that must handle high-volume traffic with strict performance and reliability requirements.

• ✗

  It eliminates the need to provide a model signature.

  Why it's wrong here

  The requirement for a model signature is determined by the MLflow model format and the serving framework, not by the chosen infrastructure type. Regardless of whether you use provisioned or serverless endpoints, you must always define a model signature for schema validation and robust inference behavior.

• ✗

  It is the only way to deploy non-Python models.

  Why it's wrong here

  Databricks supports a variety of models through its MLflow integration, and infrastructure selection (provisioned vs. serverless) does not restrict the programming language or model type. The core requirement is that the model is correctly logged using the appropriate MLflow flavor to be served regardless of deployment mode.