Free Databricks Certified Machine Learning Professional Exam Databricks-Machine-Learning-Professional Exam Practice Test

Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?

• The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application's current scenario
• The context parameter allows the user to document the performance of a model after it has been deployed
• The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model
• The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario
• The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files

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

Which of the following is a benefit of logging a model signature with an MLflow model?

• The model will have a unique identifier in the MLflow experiment
• The schema of input data can be validated when serving models
• The model can be deployed using real-time serving tools
• The model will be secured by the user that developed it
• The schema of input data will be converted to match the signature

Which of the following describes label drift?

• Label drift is when there is a change in the distribution of the predicted target given by the model
• None of these describe label drift
• Label drift is when there is a change in the distribution of an input variable
• Label drift is when there is a change in the relationship between input variables and target variables
• Label drift is when there is a change in the distribution of a target variable

A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

• Because the streaming deployment is always on, all types of data must be handled without producing an error
• All of these statements
• Because the streaming deployment is always on, there is no practitioner to debug poor model performance
• Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
• None of these statements

A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time.Which of the following deployment strategies can be used to meet these requirements?

• Edge/on-device
• Streaming
• None of these strategies will meet the requirements.
• Batch
• Real-time

After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.Which of the following SQL commands can be used to accomplish this task?

• VERSION
• DESCRIBE
• HISTORY
• DESCRIBE HISTORY
• TIMESTAMP

Which of the following machine learning model deployment paradigms is the most common for machine learning projects?

• On-device
• Streaming
• Real-time
• Batch
• None of these deployments

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?

• mlflow.log_artifact
• mlflow.log_model
• mlflow.log_metric
• mlflow.log_param
• There is no way to store values like this.

A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables.Which of the following tools can the machine learning engineer use to assess their theory?

• Kolmogorov-Smirnov (KS) test
• One-way Chi-squared Test
• Two-way Chi-squared Test
• Jenson-Shannon distance
• None of these