[Sep 21, 2026] Get Latest and 100% Accurate Databricks-Machine-Learning-Professional Exam Questions [Q68-Q84]

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[Sep 21, 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	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
Topic 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 3	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
Topic 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 5	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
Topic 6	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 7	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments

QUESTION 68
Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

All of these reasons

JS is not normalized or smoothed

None of these reasons

JS is more robust when working with large datasets

JS does not require any manual threshold or cutoff determinations

QUESTION 69
A Machine Learning Engineer is implementing integration tests for an ML pipeline in Databricks.
The current integration test runs the complete workflow but takes four hours to execute due to large dataset processing and extensive model training. They need to select an approach that will be the most effective for optimizing integration test execution while maintaining test reliability. The approach should also be based on MLOps best practices. Which approach will do this?

Run integration tests only in the production environment using full datasets to ensure complete accuracy of the testing process.

Use small subsets of production-like data and reduce training iterations while maintaining the same pipeline structure and validation checkpoints in a staging environment that closely matches production.

Skip the model training phase entirely and only test data transformations and API endpoints using mock model predictions.

Replace integration tests with unit tests for each pipeline component to reduce execution time and focus on individual component validation in a staging environment that closely matches production.

QUESTION 70
Why is Apache Spark useful for machine learning training?

GPU rendering

Distributed data processing

Web serving

Data visualization

QUESTION 71
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

features_df.write.mode(“fs”).path(“new_table”)

features_df.write.mode(“feature”).path(“new_table”)

QUESTION 72
What is commonly monitored in deployed ML models?

GPU usage only

Model drift and prediction quality

Code style

Notebook formatting

QUESTION 73
A Machine Learning Engineer developed a dynamic pricing model in MLflow that requires values from the company’s cloud database to generate predictions. At inference time the PyFunc model uses the cloud provider’s Python SDK to retrieve the latest values from the database. The inference code works well in a notebook, but when the engineer deploys the model to Databricks Model Serving, they receive 401 errors saying the user is not authenticated when trying to access the database. The engineer deploys the code via the REST API with the following payload:

Their Databricks administrators store cloud credentials under a Databricks secret scope called
“cloud_creds” with key “db_key”. These credentials can be used to authenticate to the cloud provider’s SDK.
Which change can the engineer make so the endpoint can authenticate to the remote database while avoiding storing the access tokens in plain text?

Retrieve the secret values using a notebook with dbutils.secrets.get(scope=”cloud_creds”, key=”db_key”). Log the model to MLflow with the access token value as a custom MLflow artifact.
Register the model in Unity Catalog. Change the PyFunc model code to read the artifact and pass the secret to the cloud provider’s SDK.

Retrieve the secret values using a notebook with dbutils.secrets.get(scope=”cloud_creds”, key=”db_key”). Add “environment_vars”:
{“DB_ACCESS_TOKEN”: “”} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider’s SDK.

Log the model to MLflow and store {{secrets/cloud_creds/db_key}} as a MLflow secret in
/.mlflow/credentials. Register the model in Unity Catalog. When deployed in Databricks Model Serving, MLflow will automatically authenticate when the model server starts.

Add “environment_vars”: {“DB_ACCESS_TOKEN”: “{{secrets/cloud_creds/db_key}}”} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider’s SDK.

QUESTION 74
A machine learning engineer has developed the following custom model class with preprocessing logic to combine two columns:

However, instances of this class are unable to compute predictions.
Which set of changes will update the class so predictions can be computed while continuing to apply the preprocessing logic?

Replace model_input.copy() with self.preprocess_input(model_input.copy()) in the preprocess_input method

Remove the self.rf_model = rf_model line from the fit method

Replace model_input.copy() with self.preprocess_input(model_input.copy()) in the predict method

Replace self.rf_model.predict(input) with self.predict(input) in the predict method

QUESTION 75
A data scientist is building a model to predict which communication channel (Phone, SMS, Email, or Post) is most likely to be effective for a given customer. Which model type is suited to this task?

Linear Regression

ARIMA

Logistic Regression

Softmax Classifier

QUESTION 76
A team is building a machine learning pipeline, which includes feature engineering, model training, model evaluation, and model deployment. Over the last year, the project has grown a lot, with multiple contributors working on it. Currently, they are manually testing their pipelines and functions. However, they are looking to build out a MLOps process and want to organize their unit tests. Which approach to organizing functions and unit tests should they take?

Organize functions and unit tests together in multiple notebooks, grouping them by feature or component.

Write unit tests only after deployment to avoid slowing down development.

Place all functions and their corresponding unit tests in a single notebook or script.

Separate functions into dedicated modules and place all unit tests in a separate test suite or notebook.

QUESTION 77
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.
Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

It is not possible to deploy a scikit-learn model on a Spark DataFrame.

QUESTION 78
A Machine Learning Engineer wants to use on-demand features to train a model. They have a Python UDF, which relies on data fetched from a FeatureLookup. However, in online serving, the pipeline falls when the lookup ID is not found. Which solution will resolve this issue?

Update the UDF so that it handles both NaNs and None values.

Add a default ID when defining the FeatureLookup, so it does not return None values.

Log the model correctly via the FeatureEngineeringClient, so it automatically evaluates on- demand features.

QUESTION 79
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.

QUESTION 80
Which stage in the MLflow Model Registry is typically used for models currently serving production traffic?

None

Staging

Production

Archived

QUESTION 81
A Machine Learning Engineer wants to monitor the quality and stability of their machine learning model’s predictions over time. They have a Delta table, retail_inference_log, which records each model prediction along with input features, a timestamp, and (when available) the true label. They need to detect data drift and monitor model performance trends using Databricks Lakehouse Monitoring, ensuring that alerts are triggered if the distribution of predictions or input features changes significantly. Which approach will set up monitoring for this use case?

Create a monitor with the Inference profile on the retail_inference_log table, specifying the timestamp column and the columns for model inputs, predictions, and labels. Configure the monitor to compute drift and performance metrics over time windows.

Create a monitor with the Inference profile on the retail_inference_log table, and specify a recent batch of production data as the baseline table for drift detection. Use this recent production data to compare against new data for drift and performance monitoring.

Create a monitor with the Snapshot profile on the retail_inference_log table, so that metrics are calculated over the entire table each time the monitor runs and therefore is able to compare new values with previous ones to compute data drift.

Create a monitor with the Time Series profile on the retail_inference_log table, specifying the timestamp column and including model input, prediction columns and the true label column. This will track drift in features and predictions over time, and model performance could also be tracked using a custom metric.

QUESTION 82
A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files. Based on the scenario, which optimization technique could speed up the query by colocating similar records while considering values in multiple columns?

Z-Ordering

Bin-packing

Write as a Parquet file

Data skipping

Tuning the file size

QUESTION 83
How can you save a trained Spark ML PipelineModel?

pipeline.save()

pipelineModel.write().save()

pipeline.persist()

pipeline.store()

QUESTION 84
A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?

Z-Ordering

Bin-packing

Write as a Parquet file

Data skipping

Tuning the file size

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[Sep 21, 2026] Get Latest and 100% Accurate Databricks-Machine-Learning-Professional Exam Questions [Q68-Q84]

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

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