Databricks-ML-Assoc Model Deployment Practice Test 3 — 15 Questions

Question 1

When deploying a model to a production environment, why is it critical to create a dedicated 'staging' environment before the 'production' environment?

Accepted Answer

To perform end-to-end integration testing in a production-like environment.. A staging environment acts as a vital quality gate that mirrors the production configuration. By testing the model in a staging environment, developers can identify integration issues, resource bottlenecks, and environmental discrepancies without impacting actual end-users. This practice reduces risk, ensures the model behaves predictably under load, and allows for thorough regression testing, which is essential for maintaining high availability and reliability in mission-critical machine learning applications.

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To increase the cost of the deployment infrastructure.

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To hide the model from the model registry.

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To store backup copies of the training dataset.

Question 2

When using Databricks Model Serving, what is the primary benefit of using a 'Provisioned Throughput' endpoint over a 'Serverless' endpoint?

Accepted Answer

It provides guaranteed performance and lower latency for heavy workloads.. Provisioned Throughput is designed for high-performance use cases requiring guaranteed performance, such as low-latency requirements or high-throughput scenarios that cannot tolerate fluctuations in resource availability. By reserving dedicated capacity, it provides the predictable performance needed for enterprise-level applications, ensuring that critical models meet their SLAs despite external load variations. This is a crucial choice for models that require consistent performance regardless of traffic volume patterns.

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

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It eliminates the need to provide a model signature.

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

Question 3

Refer to the exhibit. What is the impact of setting 'auto_capture_request_payload' to true?

Accepted Answer

It stores incoming request data in a table for analysis and monitoring.. Enabling 'auto_capture_request_payload' is a powerful feature for debugging, auditing, and continuous model improvement. It allows teams to log the exact data received by the endpoint, enabling them to analyze prediction inputs, perform retrospective bias analysis, and verify data quality. This is a critical component of a robust MLOps strategy, as it provides the raw data needed to identify the causes of poor predictions and justify model decisions for compliance.

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It automatically retrains the model whenever new data is captured.

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It limits the endpoint to only accept JSON payloads.

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It encrypts the payload before it reaches the model.

Question 4

A data scientist has registered a scikit-learn model in the Databricks Model Registry as `prod.churn_model`. The production endpoint serving this model must automatically roll back to the previously served version if the newly deployed version's error rate exceeds a threshold within one hour of deployment. Which Databricks feature should the data scientist configure to meet this requirement?

Accepted Answer

Set up a Databricks Lakehouse Monitoring quality monitor on the endpoint's inference table with a custom metric and an alert that triggers a rollback via a job.. Automatic rollback based on runtime error rate is not a built-in Model Serving feature. The viable path is to monitor the endpoint's inference table with Lakehouse Monitoring, define a custom metric for error rate, and use an alert to trigger a job that reconfigures the endpoint to the previous version. This leverages Databricks-native monitoring and orchestration to meet the requirement.

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Use MLflow Model Registry webhooks to trigger a rollback when the endpoint's error rate exceeds the threshold.

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Configure the endpoint to serve the new version with a small percentage of traffic and manually promote it after validating the error rate.

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Enable automatic model version rollback on the serving endpoint by defining a rollback policy with the error-rate threshold.

Question 5

A machine learning engineer is deploying a scikit-learn model to a Databricks Model Serving endpoint. The model was logged with MLflow using the default signature and input example. After deployment, the engineer notices that the endpoint's REST API expects a JSON payload in a specific format. Which MLflow artifact is used by Model Serving to determine the expected input format for the endpoint?

Accepted Answer

The MLmodel file's signature field, which contains the input schema.. Model Serving relies on the MLflow model's signature, stored in the MLmodel file, to understand the expected input schema. This signature includes column names, data types, and shapes, allowing the endpoint to validate and parse JSON requests correctly. Without a signature, the endpoint may not enforce input validation, leading to errors. The signature is essential for seamless deployment.

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The conda.yaml file, which lists the Python dependencies.

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The requirements.txt file, which pins package versions.

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The input_example.json file, which provides a sample input.

Question 6

A team deploys an MLflow pyfunc model to a Databricks Model Serving endpoint. During pre-deployment testing they call the endpoint with a small batch of records and receive an HTTP 400 error stating the request payload does not match the model signature. The model was logged with an inferred signature from a pandas DataFrame. Which action most directly resolves the mismatch?

Accepted Answer

Re-log the model with an explicit input_example and signature, then register the new version and update the served entity to that version.. A signature mismatch means the logged model's expected schema differs from the payload the endpoint receives. The reliable fix is to re-log the model with an explicit signature and input example, register the new version, and repoint the served entity. Compute scaling, content-type changes, and inference tables address different concerns and do not alter schema validation.

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Enable inference tables on the endpoint so failed requests are logged and the signature is auto-corrected.

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Increase the endpoint's workload size from Small to Large so the request payload can be accepted.

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Add a `Content-Type: text/plain` header to the request so the endpoint parses the payload differently.

Question 7

A team has an existing Databricks Model Serving endpoint serving `prod.ml.fraud_model` version 3. They register version 4, which uses a new feature set, and want to shift only 10% of traffic to version 4 while keeping version 3 for the rest. Their endpoint currently has a single served entity for version 3. What is the most appropriate approach?

Accepted Answer

Add a second served entity for version 4 to the same endpoint and configure traffic splitting between the two served entities.. Traffic splitting in Databricks Model Serving is achieved by defining multiple served entities on a single endpoint and assigning each a traffic percentage. Adding a served entity for version 4 at 10% and leaving version 3 at 90% implements the desired canary rollout. Swapping the served entity version, using an external load balancer, or renaming the model do not provide the controlled split.

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Register version 4 under a new model name and point the existing served entity at the new model name.

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Update the existing served entity's entity_version to 4 and rely on the endpoint's automatic gradual rollout.

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Create a second endpoint for version 4 and configure a load balancer outside Databricks to split traffic 10/90 between endpoints.

Question 8

A machine learning engineer has registered a model in the Databricks Model Registry and wants to expose it as a REST API with automatic scaling and no server management. The model's Python dependencies are captured in a conda environment file logged with the run. Which Databricks capability should the engineer use to serve this model with minimal operational overhead?

Accepted Answer

Databricks Model Serving with a model version URI from the Model Registry. Databricks Model Serving is the managed capability that takes a registered model version URI, reconstructs the environment from logged dependencies, and publishes a scalable REST endpoint without server administration. The other choices describe batch, interactive, or SQL compute that cannot deliver managed low-latency REST inference for a registered MLflow model.

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A Databricks job that runs the model on a schedule and writes predictions to a Delta table

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A SQL warehouse configured with the model's conda environment installed on all nodes

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An all-purpose cluster running an MLflow model server process started manually

Question 9

A data scientist registers a scikit-learn model in Unity Catalog as `prod.ml.churn_model`. They then create a Databricks Model Serving endpoint via the REST API using `served_entities` with `entity_name` set to `prod.ml.churn_model` and `entity_version` set to `"3"`. The endpoint creation fails. What is the most likely cause?

Accepted Answer

The serving endpoint lacks USE CATALOG, USE SCHEMA, and EXECUTE privileges on the Unity Catalog model, so the service principal cannot read the model artifacts.. Serving a Unity Catalog model requires the serving identity to hold USE CATALOG, USE SCHEMA, and EXECUTE privileges on the catalog, schema, and model. Without these grants, the endpoint cannot retrieve the model version, and creation fails. Referencing the model by three-level name and version string is otherwise correct, so permissions are the most likely root cause.

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Model Serving only supports models registered in the workspace Model Registry, not models registered in Unity Catalog.

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The model version must be referenced as a numeric integer, but the field only accepts a string, so the deployment cannot resolve the artifact path.

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The model must first be exported to DBFS as an MLflow artifact tarball before it can be referenced by a serving endpoint.

Question 10

A machine learning engineer is configuring a Databricks Model Serving endpoint for a model that requires GPU acceleration. They set the workload size to 'GPU_Medium' but the endpoint fails to deploy. Which of the following is the most likely cause of the failure?

Accepted Answer

The Databricks workspace does not have GPU instances enabled or available.. GPU workload sizes for Model Serving require the workspace to have GPU instances enabled and available. If the workspace lacks GPU capacity or the user lacks permissions, deployment fails. Other factors like flavor or signature are unrelated to GPU provisioning. Ensuring GPU availability is a prerequisite for GPU-accelerated endpoints.

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The model was not logged with the 'mlflow.pyfunc' flavor.

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The model's signature does not include GPU-specific metadata.

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The endpoint was configured with 'min_instances' set to 0.

Question 11

A data scientist has registered a scikit-learn model in Unity Catalog and now wants to serve it behind a Databricks Model Serving endpoint. The model's MLflow signature records a pandas DataFrame input with three named columns. The team wants the endpoint to reject malformed requests automatically rather than silently scoring them. Which action should the data scientist take?

Accepted Answer

Create the endpoint with the registered model version; Model Serving enforces the logged MLflow signature and schema validation by default.. When a model is logged with an MLflow signature, Databricks Model Serving uses that signature to validate incoming requests. A pandas DataFrame signature with named columns causes the endpoint to check column names and types, rejecting payloads that do not conform. No custom preprocessing or inference table configuration is required, because validation happens at the serving layer before the model is invoked.

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Enable inference tables on the endpoint so malformed records are flagged in the Delta table and excluded from scoring.

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Register the model with a new signature that uses a tensor spec instead of a DataFrame spec so the endpoint validates each element.

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Add a Python preprocessing script that parses the raw JSON and raises an error for unexpected fields, then deploy that script as the endpoint's model.

Question 12

A team wants to route production traffic to a new model version while keeping risk low. They configure a Databricks Model Serving endpoint with two served entities: `champion` (entity_version 5) and `challenger` (entity_version 6). They want 95% of requests to hit `champion` and 5% to hit `challenger`. Which configuration accomplishes this?

Accepted Answer

Set `traffic_config` with routes assigning `traffic_percentage` 95 to `champion` and 5 to `challenger`.. Databricks Model Serving uses `traffic_config` with a `routes` list, where each route references a served entity by name and assigns an integer `traffic_percentage`. Summing route percentages to 100 achieves the desired 95/5 split. Other configuration fields like workload size and scale-to-zero govern resources and scaling behavior, not request distribution.

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Deploy two separate endpoints and use a client-side load balancer to send 95% of calls to the champion endpoint.

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Set `scale_to_zero_enabled` to true only on `challenger` so it receives proportionally fewer requests.

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Set `workload_size` to `Small` on `champion` and `Large` on `challenger` to bias traffic toward the larger entity.

Question 13

A team maintains a Databricks Model Serving endpoint for a fraud model. Compliance requires that every request and response be logged to a Delta table for auditing and later analysis. The endpoint is already configured and serving traffic. What should the team do to capture this data with the least additional infrastructure?

Accepted Answer

Enable inference tables on the endpoint so requests and responses are automatically logged to a Delta table. Inference tables are the native Model Serving mechanism that records request and response payloads into a Delta table, giving complete server-side auditing without extra infrastructure. Client-side logging, dashboard metrics, and event logs each capture only partial or operational data, so they cannot satisfy the requirement to log every request and response for the fraud endpoint.

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Configure the endpoint to emit metrics to a monitoring dashboard and export the dashboard data nightly

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Write a client wrapper that logs each request and response to a Delta table before returning the prediction

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Attach an event log delivery to the serving endpoint and parse the logs into a Delta table with a scheduled job

Question 14

A data science team is deploying a model to a Databricks Model Serving endpoint. They want to enable inference logging to capture the input data and predictions for monitoring and debugging. Which TWO configurations are required to enable inference logging for a serving endpoint? (Choose two.)

Accepted Answer

Specify a Delta table to store the inference logs.. To enable inference logging for a Model Serving endpoint, you must explicitly enable it in the endpoint configuration and specify a Delta table to store the logs. The endpoint's identity needs write access to that table. Other factors like workload size or model signature do not affect logging. This allows capturing request and response data for monitoring and debugging.

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Set the endpoint's workload size to at least 'Medium'.

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Ensure the model signature includes a field for logging.

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Configure the model to output logs in JSON format.

Question 15

A team operates a Databricks Model Serving endpoint with min_instances set to 0 and max_instances set to 4. During a nightly batch job, the endpoint receives a burst of requests and some clients observe elevated latency. The team wants to keep costs low during idle periods while reducing cold-start latency during bursts. Which configuration change best achieves this?

Accepted Answer

Set min_instances to 1 and leave max_instances at 4 so at least one instance is always warm.. Model Serving endpoints scale between min_instances and max_instances. With min_instances at 0, the endpoint scales to zero when idle and must cold-start a replica when traffic resumes, causing the observed latency. Setting min_instances to 1 keeps a single warm replica available for immediate responses while still allowing scale-out up to max_instances during bursts, achieving both cost and latency goals.

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Set max_instances to 8 so the endpoint can scale further during the burst.

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Set min_instances to 4 so the endpoint always matches the maximum capacity.

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Enable scale-to-zero by setting both min_instances and max_instances to 0 and rely on queueing.