Free Databricks Certified Machine Learning Professional Exam Databricks-Machine-Learning-Professional Exam Practice Test

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

• mlflow.shap.log_explanation
• None of these operations can accomplish the task.
• mlflow.shap
• mlflow.log_figure
• client.log_artifact

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?

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

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

• The model will have a unique identifier in the MLflow experiment
• The schema of input data can be validated when serving models
• The model can be deployed using real-time serving tools
• The model will be secured by the user that developed it
• The schema of input data will be converted to match the signature

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

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

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?

• mlflow.spark.load_model(model_uri)
• mlflow.pyfunc.read_model(model_uri)
• mlflow.sklearn.read_model(model_uri)
• mlflow.pyfunc.load_model(model_uri)
• mlflow.sklearn.load_model(model_uri)

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?

• Startinq a manual parent run before calling fmin
• Ensuring that a built-in model flavor is used for the model logging
• Starting a manual child run within the objective function
• There is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt
• MLflow Autoloqqinq will automatically accomplish this task with Hyperopt

A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?

• Z-Ordering
• Bin-packing
• Write as a Parquet file
• Data skipping
• Tuning the file size

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?

• mlflow.models.schema.infer_schema
• mlflow.models.signature.infer_signature
• mlflow.models.Model.get_input_schema
• mlflow.models.Model.signature
• There is no way to obtain the input schema and the output schema of an unlogged model.

Which of the following describes concept drift?

• Concept drift is when there is a change in the distribution of an input variable
• Concept drift is when there is a change in the distribution of a target variable
• Concept drift is when there is a change in the relationship between input variables and target variables
• Concept drift is when there is a change in the distribution of the predicted target given by the model
• None of these describe Concept drift

Which of the following lists all of the model stages are available in the MLflow Model Registry?

• Development. Staging. Production
• None. Staging. Production
• Staging. Production. Archived
• None. Staging. Production. Archived
• Development. Staging. Production. Archived