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

• Question 1

  A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They are trying to determine how to manage nested Hyperopt runs with MLflow Autologging.Which of the following approaches will create a single parent run for the process and a child run for each unique combination of hyperparameter values when using Hyperopt and MLflow Autologging?

  • Startinq a manual parent run before calling fmin
  • Ensuring that a built-in model flavor is used for the model logging
  • Starting a manual child run within the objective function
  • There is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt
  • MLflow Autoloqqinq will automatically accomplish this task with Hyperopt

  Show Answer Answer: A Next

• Question 2

  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

  Show Answer Answer: D Next

• Question 3

  Which of the following is a simple statistic to monitor for categorical feature drift?

  • Mode
  • None of these
  • Mode, number of unique values, and percentage of missing values
  • Percentage of missing values
  • Number of unique values

  Show Answer Answer: C Next

• Question 4

  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

  Show Answer Answer: C Next

• Question 5

  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

  Show Answer Answer: A Next