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

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

Posted on September 27, 2026 By freedumps No Comments on [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?

 
 
 
 
The correct way to retrieve logged metrics for a specific run using the MLflow Client is client.get_run(run_id).data.metrics. This returns a dictionary of all metrics logged for that run. The method in the image (mlflow.search_runs(…)) is for querying across multiple runs, not for accessing a specific run’s metrics.

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?

 
 
 
 
This approach uses a Spark Structured Streaming read from the continuously updated Delta table and applies an MLflow-registered Spark UDF for inference. The model execution is distributed across the three executors, enabling parallel, low-latency scoring as new data arrives. Writing the results with writeStream efficiently updates the forecast_results Delta table incrementally, maximizing cluster utilization and aligning with best practices for continuous, scalable batch- stream inference in Databricks.

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

 
 
 
 
Streaming machine learning applications face multiple challenges, including end-to-end fault tolerance (ensuring recovery from failures without data loss) and out-of-order data (handling events that arrive late or out of sequence). Both are common obstacles in building reliable real- time ML systems.

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?

 
 
 
 
 

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?

 
 
 
 
 

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

 
 
 
 
 

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?

 
 
 
 
 

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?

 
 
 
 
Using write_table with mode set to merge updates existing rows and inserts new ones based on the table’s primary key. This ensures that the latest feature values for each customer_id are merged into the existing feature table each day without overwriting the entire table, which is the correct and scalable approach for maintaining up-to-date customer features in Unity Catalog.

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

 
 
 
 
In batch deployment, predictions are precomputed at scheduled intervals and stored for later querying. This approach is ideal when real-time inference is not required and when predictions can be generated in bulk ahead of time.

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?

 
 
 
 
 

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

 
 
 
 
 

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

 
 
 
 
CrossValidator performs:
parameter search
k-fold cross validation.

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?

 
 
 
 
When using Ray Tune with MLflow on Databricks, Ray workers must be able to authenticate back to the Databricks workspace to log runs to MLflow Tracking. Setting the DATABRICKS_HOST and DATABRICKS_TOKEN environment variables before initializing the Ray cluster ensures all Ray processes can securely communicate with Databricks and correctly log MLflow experiments during distributed hyperparameter tuning.

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

 
 
 
 
 

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?

 
 
 
 
The artifacts parameter in mlflow.pyfunc.log_model is designed for packaging non-code assets required at inference time, such as tokenizer files. By logging the tokenizer as a model artifact and referencing its path, MLflow ensures the file is versioned with the model and automatically made available to Databricks Model Serving during inference.

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?

 
 
 
 
When drift is detected, it indicates that the data distribution has changed. Therefore, the new model must perform better than the original model on the most recently available data, as that data reflects current conditions. This ensures the new model is well-adapted to the current environment and will deliver more reliable predictions.

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