Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?

A. fs.create_table

B. fs.write_table

C. fs.get_table

D. There is no way to accomplish this task with fs

E. fs.read_table

A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the

original model object.

Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

A. mlflow.load_model(model_uri)

B. client.list_artifacts(run_id)["feature-importances.csv"]

C. mlflow.sklearn.load_model(model_uri)

D. This can only be viewed in the MLflow Experiments UI

E. client.pyfunc.load_model(model_uri)

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

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.

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)

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")

In a continuous integration, continuous deployment (CI/CD) process for machine learning pipelines, which of the following events commonly triggers the execution of automated testing?

A. The launch of a new cost-efficient SQL endpoint

B. CI/CD pipelines are not needed for machine learning pipelines

C. The arrival of a new feature table in the Feature Store

D. The launch of a new cost-efficient job cluster

E. The arrival of a new model version in the MLflow Model Registry

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 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

A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df. Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

A. Option A

B. Option B

C. Option C

D. Option D

E. Option E