DATABRICKS-MACHINE-LEARNING-PROFESSIONAL Online Practice Questions and Answers

A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:

1.

Deploy a model to production and compute predicted values

2.

Obtain the observed (actual) label values

3.

_____

4.

Run a statistical test to determine if there are changes over time Which of the following should be completed as Step #3?

A. Obtain the observed values (actual) feature values

B. Measure the latency of the prediction time

C. Retrain the model

D. None of these should be completed as Step #3

E. Compute the evaluation metric using the observed and predicted values

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

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

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.

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.

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

Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?

A. The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application's current scenario

B. The context parameter allows the user to document the performance of a model after it has been deployed

C. The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model

D. The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario

E. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files

A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.

They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.

Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

A. Indent the child run blocks within the parent run block

B. Add the nested=True argument to the parent run

C. Remove the nested=True argument from the child runs

D. Provide the same name to the run_name parameter for all three run blocks

E. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs

A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project "model" and they'd like to add

the text in the model_description variable.

The team is using the following line of code:

Which of the following changes does the team need to make to the above code block to accomplish the task?

A. Replace update_registered_model with update_model_version

B. There no changes necessary

C. Replace description with artifact

D. Replace client.update_registered_model with mlflow

E. Add a Python model as an argument to update_registered_model

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