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

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

• Question 51:

  Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?

  A. Starting a testing job when a new model is registered
  B. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
  C. Sending an email alert when an automated testing Job fails
  D. None of these use cases require the use of an HTTP Webhook
  E. Sending a message to a Slack channel when a model version transitions stages
  E. Sending a message to a Slack channel when a model version transitions stages

• Question 52:

  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?

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

• Question 53:

  Which of the following is a simple, low-cost method of monitoring numeric feature drift?

  A. Jensen-Shannon test
  B. Summary statistics trends
  C. Chi-squared test
  D. None of these can be used to monitor feature drift
  E. Kolmogorov-Smirnov (KS) test
  E. Kolmogorov-Smirnov (KS) test

• Question 54:

  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?

  A. Spark UDFs
  B. Structured Streaming
  C. MLflow
  D. Delta Lake
  E. AutoML
  B. Structured Streaming

• Question 55:

  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?

  A. Staging, Production, Archived
  B. Production
  C. None, Staging, Production, Archived
  D. Staging, Production
  E. None, Staging, Production
  D. Staging, Production

• Question 56:

  A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df,

  but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.

  Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

  A. df = fs.get_missing_features(spark_df, model_uri) fs.score_model(model_uri, df)
  B. fs.score_model(model_uri, spark_df)
  C. df = fs.get_missing_features(spark_df, model_uri) fs.score_batch(model_uri, df)
  D. df = fs.get_missing_features(spark_df) fs.score_batch(model_uri, df)
  E. fs.score_batch(model_uri, spark_df)
  E. fs.score_batch(model_uri, spark_df)

• Question 57:

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

  A. Mode
  B. None of these
  C. Mode, number of unique values, and percentage of missing values
  D. Percentage of missing values
  E. Number of unique values
  C. Mode, number of unique values, and percentage of missing values

• Question 58:

  A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline. Which of the following Databricks tools can be used to programmatically create the Job?

  A. MLflow APIs
  B. AutoML APIs
  C. MLflow Client
  D. Jobs cannot be created programmatically
  E. Databricks REST APIs
  E. Databricks REST APIs

• Question 59:

  Which of the following Databricks-managed MLflow capabilities is a centralized model store?

  A. Models
  B. Model Registry
  C. Model Serving
  D. Feature Store
  E. Experiments
  B. Model Registry

• Question 60:

  A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage.

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

  

  A. Option A
  B. Option B
  C. Option C
  D. Option D
  E. Option E
  D. Option D