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

• Question 11:

  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

  Correct Answer: B

• Question 12:

  After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set. Which of the following SQL commands can be used to accomplish this task?

  A. VERSION

  B. DESCRIBE

  C. HISTORY

  D. DESCRIBE HISTORY

  E. TIMESTAMP

  Correct Answer: D

• Question 13:

  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

  Correct Answer: E

• Question 14:

  Which of the following machine learning model deployment paradigms is the most common for machine learning projects?

  A. On-device

  B. Streaming

  C. Real-time

  D. Batch

  E. None of these deployments

  Correct Answer: D

• Question 15:

  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()

  Correct Answer: E

• Question 16:

  A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.

  They write the following incomplete code block:

  image9

  Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

  A. log_artifact

  B. log_model

  C. log_metric

  D. log_param

  E. There is no way to store values like this.

  Correct Answer: C

• Question 17:

  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?

  A. mlflow.models.schema.infer_schema

  B. mlflow.models.signature.infer_signature

  C. mlflow.models.Model.get_input_schema

  D. mlflow.models.Model.signature

  E. There is no way to obtain the input schema and the output schema of an unlogged model.

  Correct Answer: B

• Question 18:

  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?

  A. Because the streaming deployment is always on, all types of data must be handled without producing an error

  B. All of these statements

  C. Because the streaming deployment is always on, there is no practitioner to debug poor model performance

  D. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale

  E. None of these statements

  Correct Answer: B

• Question 19:

  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

  Correct Answer: E

• Question 20:

  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

  Correct Answer: B