Databricks Databricks-Machine-Learning-Professional 問題集

質問 1：
Which of the following is a benefit of logging a model signature with an MLflow model?
A. The schema of input data will be converted to match the signature
B. The model can be deployed using real-time serving tools
C. The model will be secured by the user that developed it
D. The model will have a unique identifier in the MLflow experiment
E. The schema of input data can be validated when serving models
正解：A

質問 2：
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. features_df.write.mode("feature").path("new_table")
B.

C. features_df.write.mode("fs").path("new_table")
D.

E.

正解：E

質問 3：
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?
A. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
df = fs.get_missing_features(spark_df)
B. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)
C. fs.score_batch(model_uri, spark_df)
D. fs.score_model(model_uri, spark_df)
E. fs.score_batch(model_uri, df)
正解：C

質問 4：
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. [None. Staging. Production
B. Production
C. Staging. Production
D. Staging. Production. Archived
E. None. Staging. Production. Archived
正解：C

質問 5：
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. Sunsetting the machine learning application
E. Rebuilding the machine learning application with a new label variable
正解：A

質問 6：
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_param
B. mlflow.log_metric
C. mlflow.log_artifact
D. mlflow.log_model
E. There is no way to store values like this.
正解：B