Certified Machine Learning Professional Practice Test

Certified Machine Learning Professional Practice Test practice questions, page 5 of 9, with answer review and explanations.

Question 21

Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?

  1. mlflow.shap.log_explanation
  2. None of these operations can accomplish the task.
  3. mlflow.shap
  4. mlflow.log_figure
  5. client.log_artifact

Question 22

A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input. Which of the following MLflow operations can be used to perform this task?

  1. mlflow.models.schema.infer_schema
  2. mlflow.models.signature.infer_signature
  3. mlflow.models.Model.get_input_schema
  4. mlflow.models.Model.signature
  5. There is no way to obtain the input schema and the output schema of an unlogged model.

Question 23

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?

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

Question 24

Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

  1. Streaming
  2. Batch
  3. Edge/on-device
  4. None of these strategies will accomplish the task.
  5. Real-time

Question 25

A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches. Which of the following tools can be used to provide this type of continuous processing?

  1. Spark UDFs
  2. Structured Streaming
  3. MLflow
  4. Delta Lake
  5. AutoML