Databricks DATABRICKS-MACHINE-LEARNING-PROFESSIONAL Online Practice Questions and Exam Preparation

Databricks DATABRICKS-MACHINE-LEARNING-PROFESSIONAL Online Questions & Answers

• Question 41:

  Which of the following is a probable response to identifying drift in a machine learning application?

  A. None of these responses
  B. Retraining and deploying a model on more recent data
  C. All of these responses
  D. Rebuilding the machine learning application with a new label variable
  E. Sunsetting the machine learning application
  B. Retraining and deploying a model on more recent data

• Question 42:

  A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is

  used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console.

  Which of the following lines of code can they use to accomplish this task?

  A. mlflow.sklearn.autolog()
  B. mlflow.spark.autolog()
  C. spark.conf.set(“autologging”, True)
  D. It is not possible to automatically log MLflow runs.
  E. mlflow.autolog()
  E. mlflow.autolog()

• Question 43:

  Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?

  A. fs.create_table
  B. fs.write_table
  C. fs.get_table
  D. There is no way to accomplish this task with fs
  E. fs.read_table
  E. fs.read_table

• Question 44:

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

  A. Streaming
  B. Batch
  C. Edge/on-device
  D. None of these strategies will accomplish the task.
  E. Real-time
  E. Real-time

• Question 45:

  Which of the following describes label drift?

  A. Label drift is when there is a change in the distribution of the predicted target given by the model
  B. None of these describe label drift
  C. Label drift is when there is a change in the distribution of an input variable
  D. Label drift is when there is a change in the relationship between input variables and target variables
  E. Label drift is when there is a change in the distribution of a target variable
  E. Label drift is when there is a change in the distribution of a target variable

• Question 46:

  Which of the following describes the concept of MLflow Model flavors?

  A. A convention that deployment tools can use to wrap preprocessing logic into a Model
  B. A convention that MLflow Model Registry can use to version models
  C. A convention that MLflow Experiments can use to organize their Runs by project
  D. A convention that deployment tools can use to understand the model
  E. A convention that MLflow Model Registry can use to organize its Models by project
  D. A convention that deployment tools can use to understand the model

• Question 47:

  Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

  A. python_function provides no benefits over the built-in library-specific model flavors
  B. python_function can be used to deploy models in a parallelizable fashion
  C. python_function can be used to deploy models without worrying about which library was used to create the model
  D. python_function can be used to store models in an MLmodel file
  E. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments
  C. python_function can be used to deploy models without worrying about which library was used to create the model

• Question 48:

  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?

  A. Edge/on-device
  B. Streaming
  C. None of these strategies will meet the requirements.
  D. Batch
  E. Real-time
  E. Real-time

• Question 49:

  A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the

  original model object.

  Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

  A. mlflow.load_model(model_uri)
  B. client.list_artifacts(run_id)["feature-importances.csv"]
  C. mlflow.sklearn.load_model(model_uri)
  D. This can only be viewed in the MLflow Experiments UI
  E. client.pyfunc.load_model(model_uri)
  C. mlflow.sklearn.load_model(model_uri)

• Question 50:

  A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri. Which of the following operations can be used to load the model as an sklearn object for batch deployment?

  A. mlflow.spark.load_model(model_uri)
  B. mlflow.pyfunc.read_model(model_uri)
  C. mlflow.sklearn.read_model(model_uri)
  D. mlflow.pyfunc.load_model(model_uri)
  E. mlflow.sklearn.load_model(model_uri)
  E. mlflow.sklearn.load_model(model_uri)