Databricks Databricks Machine Learning Professional Practice Exams
Last updated on Oct 22,2026- Exam Code: Databricks Machine Learning Professional
- Exam Name: Databricks Certified Machine Learning Professional
- Certification Provider: Databricks
- Latest update: Oct 22,2026
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:
The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?
- A . Indent the child run blocks within the parent run block
- B . Add the nested=True argument to the parent run
- C . Remove the nested=True argument from the child runs
- D . Provide the same name to the run name parameter for all three run blocks
- E . Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
In a continuous integration, continuous deployment (CI/CD) process for machine learning pipelines, which of the following events commonly triggers the execution of automated testing?
- A . The launch of a new cost-efficient SQL endpoint
- B . CI/CD pipelines are not needed for machine learning pipelines
- C . The arrival of a new feature table in the Feature Store
- D . The launch of a new cost-efficient job cluster
- E . The arrival of a new model version in the MLflow Model Registry
A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project".
Which of the following MLflow operations can the machine learning engineer use to accomplish this task?
- A . mlflow.register_model
- B . MlflowClient.update_registered_model
- C . mlflow.add_model_version
- D . MlflowClient.get_model_version
- E . The machine learning engineer needs to create an entirely new MLflow Model Registry model
A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run.
Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?
- A . mlflow.log_artifact
- B . mlflow.log_model
- C . mlflow.log_metric
- D . mlflow.log_param
- E . There is no way to store values like this.
Which of the following machine learning model deployment paradigms is the most common for machine learning projects?
- A . On-device
- B . Streaming
- C . Real-time
- D . Batch
- E . None of these deployments
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?
- A . https://<databricks-instance>/model-serving/recommender/Production/invocations
- B . The version number of the model version in Production is necessary to complete this task.
- C . https://<databricks-instance>/model/recommender/stage-production/invocations
- D . https://<databricks-instance>/model-serving/recommender/stage-production/invocations
- E . https://<databricks-instance>/model/recommender/Production/invocations
Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?
- A . Obtain the observed values (actual) feature values
- B . Measure the latency of the prediction time
- C . Retrain the model
- D . None of these should be completed as Step #3
- E . Compute the evaluation metric using the observed and predicted values
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model.
Which of the following code blocks will accomplish this task inside of an existing MLflow run block?
- A .
- B .
- C . mlflow.log_data(importance_path, "feature-importance.csv")
- D . mlflow.log_artifact(importance_path, "feature-importance.csv")
- E . None of these code blocks tan accomplish the task.
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?
- A . Z-Ordering
- B . Bin-packing
- C . Write as a Parquet file
- D . Data skipping
- E . Tuning the file size
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client.
Which of the following code blocks can they use to accomplish the task?
A)
B)
C)
D)
E)
- A . Option A
- B . Option B
- C . Option C
- D . Option D
- E . option E