Certified Machine Learning Professional Practice Test

Certified Machine Learning Professional Practice Test practice questions, page 6 of 9, with answer review and explanations.

Question 26

A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model. Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?

  1. Staging, Production, Archived
  2. Production
  3. None, Staging, Production, Archived
  4. Staging, Production
  5. None, Staging, Production

Question 27

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?

  1. mlflow.log_artifact
  2. mlflow.log_model
  3. mlflow.log_metric
  4. mlflow.log_param
  5. There is no way to store values like this.

Question 28

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?

  1. Starting a manual parent run before calling fmin
  2. Ensuring that a built-in model flavor is used for the model logging
  3. Starting a manual child run within the objective_function
  4. There is no way to accomplish nested runs with MLflow Autologging and Hyperopt
  5. MLflow Autologging will automatically accomplish this task with Hyperopt

Question 29

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?

  3. features_df.write.mode("fs").path("new_table")
  5. features_df.write.mode("feature").path("new_table")

Question 30

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

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