[Sep 27, 2026] Get Latest and 100% Accurate Databricks-Machine-Learning-Professional Exam Questions [Q36-Q51]

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[Sep 27, 2026] Get Latest and 100% Accurate Databricks-Machine-Learning-Professional Exam Questions

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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Topic	Details
Topic 1	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 2	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 3	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 4	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 5	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 6	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 7	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 8	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift

NO.36 A data scientist is utilizing MLflow to track their machine learning experiments. After completing a run with run ID run_id for the experiment with experiment ID exp_id, the data scientist wants to programmatically return the logged metrics for run_id. They have an active MLflow Client client and an active Spark session spark. Which lines of code can be used to return the logged metrics for run_id?

client.qet_run(exp_id.run_id).data.metrics

client.get_run(run_id).data.metrics

spark.read.format(“mlflow-run”).load(run_id)

NO.37 A Data Scientist needs to perform inference on a continuously updated Delta table called sales_data using an MLflow-registered Spark ML pipeline model (catalog.prod.sales_forecaster).
Predictions must be written to a Delta table forecast_results, which must be updated with low latency leveraging a cluster with three executors. They want to maximize the efficient use of their cluster when doing this. Which approach will suit their needs?

NO.38 Which of the following is an obstacle related to streaming machine learning applications?

End-to-end fault tolerance

None of these

All of these

Out-of-order data

NO.39 A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console. Which of the following lines of code can they use to accomplish this task?

mlflow.sklearn.autolog()

mlflow.spark.autolog()

spark.conf.set(“autologging”, True)

It is not possible to automatically log MLflow runs.

mlflow.autolog()

NO.40 A machine learning engineer has deployed a model recommender using MLflow Model Serving. They now want to query the version of that model that is in the Production stage of the MLflow Model Registry.
Which of the following model URIs can be used to query the described model version?

https://<databricks-instance>/model-serving/recommender/Production/invocations

The version number of the model version in Production is necessary to complete this task.

https://<databricks-instance>/model/recommender/stage-production/invocations

https://<databricks-instance>/model-serving/recommender/stage-production/invocations

https://<databricks-instance>/model/recommender/Production/invocations

NO.41 Which statement describes streaming with Spark as a model deployment strategy?

The inference of batch processed records as soon as a trigger is hit

The inference of all types of records in real-time

The inference of batch processed records as soon as a Spark job is run

The inference of incrementally processed records as soon as trigger is hit

The inference of incrementally processed records as soon as a Spark job is run

NO.42 A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark.
Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?

client.list_run_infos(exp_id)

spark.read.format(“delta”).load(exp_id)

There is no way to programmatically return row-level results from an MLflow Experiment.

mlflow.search_runs(exp_id)

spark.read.format(“mlflow-experiment”).load(exp_id)

NO.43 A Machine Learning Engineer is tasked with building an automated daily pipeline that updates a customer_features table in Unity Catalog. They have implemented a function, compute_customer_features, that returns a DataFrame with a unique customer_id as the primary key and want to ensure the latest feature values are merged into the table each day. Which code snippet implements this requirement?

NO.44 Which of the following describes batch deployment for machine learning projects?

Predictions are computed and delivered as soon as feature values are available

None of these describe batch deployment for machine learning projects

Predictions are computed prior to delivery and stored for later querying

Predictions are computed immediately as data arrives and stored for later querying

NO.45 A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name “model”. Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name “best_model”.
Which of the following lines of code can they use to register the model to the MLflow Model Registry?

mlflow.register_model(model_uri, “best_model”)

mlflow.register_model(run_id, “best_model”)

mlflow.register_model(f”runs:/{run_id}/best_model”, “model”)

mlflow.register_model(model_uri, “model”)

mlflow.register_model(f”runs:/{run_id}/model”)

NO.46 Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?

client.transition_model_version_stage

client.delete_model_version

client.update_registered_model

client.delete_model

client.delete_registered_model

NO.47 Which Spark ML class supports automated hyperparameter tuning?

ParamGridBuilder

CrossValidator

PipelineModel

BinaryClassificationEvaluator

NO.48 A Machine Learning Engineer is conducting hyperparameter tuning for multiple XGBoost models using Ray Tune on Databricks. They want to integrate MLflow tracking to monitor their experiments and need to ensure proper authentication. The engineer has Ray 2.41 installed and wants to use both Ray Tune and MLflow together in their distributed tuning workflow. They have to configure Databricks to run the hyperparameter optimization with MLflow integration. Which set of configuration steps will do this?

Install the MLflow Ray plugin using %pip install mlflow-ray and configure the workspace connection.

Configure DATABRICKS_HOST and DATABRICKS_TOKEN environment variables before calling setup_ray_cluster().

Enable MLflow autologging with mlflow.ray.autolog() and set the tracking server URI.

Set MLFLOW_TRACKING_URI and MLFLOW_EXPERIMENT_TD environment variables before initializing Ray.

NO.49 Which of the following describes the concept of MLflow Model flavors?

A convention that deployment tools can use to wrap preprocessing logic into a Model

A convention that MLflow Model Registry can use to version models

A convention that MLflow Experiments can use to organize their Runs by project

A convention that deployment tools can use to understand the model

A convention that MLflow Model Registry can use to organize its Models by project

NO.50 A Machine Learning Engineer needs to deploy a custom model using Databricks Model Serving.
The model requires an external tokenizer file (for example, a vocabulary or pre-trained tokenizer) to function correctly. They need to ensure this tokenizer file is included with the model so it is available during model serving. How should they package this tokenizer file as part of the model deployment?

Include tokenizer file as part of the model’s software dependencies by adding it to the conda_env specification.

Use the code_paths parameter in mlflow.pyfunc.log_model() to include the tokenizer file, assuming it’s part of the custom Python logic needed by the model.

Access the tokenizer file directly from Volumes at model inference time, which requires pre- provisioning access to the Volume for every model serving endpoint.

Use the artifacts parameter in mlflow.pyfunc.log_model() to package the tokenizer file, referencing its path in Volumes.

NO.51 A machine learning engineer has found drift in a production machine learning application. The engineer has determined that retraining and deploying a new model is necessary. Which statement must be true prior to deploying the new model?

All of these statements must be true prior to deploying the new model

The new model must perform better than the original model on a random subset of the available data

The now model must perform better than the original model on all of the available data

The new model must perform better than the original model on the most recently available data