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

• Question 21:

  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

  Correct Answer: D

• Question 22:

  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?

  A. mlflow.log_artifact

  B. mlflow.log_model

  C. mlflow.log_metric

  D. mlflow.log_param

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

  Correct Answer: D

• Question 23:

  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

  Correct Answer: A

• Question 24:

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

  A. The model will have a unique identifier in the MLflow experiment

  B. The schema of input data can be validated when serving models

  C. The model can be deployed using real-time serving tools

  D. The model will be secured by the user that developed it

  E. The schema of input data will be converted to match the signature

  Correct Answer: B

• Question 25:

  A machine learning engineer has deployed a model recommender using MLflow Model Serving. They now want to query the version of that model that is in the Production stage of the MLflow Model Registry. Which of the following model URIs can be used to query the described model version?

  A. https:///model-serving/recommender/Production/invocations

  B. The version number of the model version in Production is necessary to complete this task.

  C. https:///model/recommender/stage-production/invocations

  D. https:///model-serving/recommender/stage-production/invocations

  E. https:///model/recommender/Production/invocations

  Correct Answer: E

• Question 26:

  Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?

  A. Cloud-based compute

  B. None of these tools

  C. REST APIs

  D. Containers

  E. Autoscaling clusters

  Correct Answer: D

• Question 27:

  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)

  Correct Answer: E

• Question 28:

  A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks. Which of the following locations in Databricks will show these data visualizations?

  A. The MLflow Model Registry Model page

  B. The Artifacts section of the MLflow Experiment page

  C. Logged data visualizations cannot be viewed in Databricks

  D. The Artifacts section of the MLflow Run page

  E. The Figures section of the MLflow Run page

  Correct Answer: D

• Question 29:

  A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.

  They write the following incomplete code block:

  image14

  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. mlflow.spark.track_model(sklearn_model, "model")

  B. mlflow.sklearn.log_model(sklearn_model, "model")

  C. mlflow.spark.log_model(sklearn_model, "model")

  D. mlflow.sklearn.load_model("model")

  E. mlflow.sklearn.track_model(sklearn_model, "model")

  Correct Answer: B

• Question 30:

  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

  Correct Answer: D