Databricks-Machine-Learning-Professional 무료 덤프문제 온라인 액세스

A machine learning engineer wants to load the data from the very first version of a Delta table from location path. Which of the following lines of code can be used to accomplish this task?

• A.spark.read.format("delta").option("version", 0).load(path)
• B.spark.read.format("delta").option("versionAsOf", 1).load(path)
• C.spark.read.format("delta").option("versionAsOf", 0).load(path)
• D.spark.read.format("delta").option("version", 1).load(path)

A machine learning engineer has developed a machine learning pipeline that produces a scikit- learn model model and computes the RMSE rmse. MAE mae, and R-squared r2 values for the test set. They now want to log these values with the MLflow run. These values are stored in the dictionary metrics.
They run the following code block:

The code block produces an error.
Which changes to the code block will successfully complete the task?

• A.Replace metrics with rmse, mae, r2
• B.Replace metrics with model
• C.Replace mlflow.log_metric with mlflow.sklearn.log_metric
• D.Replace log_metric with log_metrics

Which of the following describes label drift?

• A.Label drift is when there is a change in the distribution of a target variable
• B.Label drift is when there is a change in the distribution of an input variable
• C.None of these describe label drift
• D.Label drift is when there is a change in the distribution of the predicted target given by the model
• E.Label drift is when there is a change in the relationship between input variables and target variables

A Machine Learning Engineer is implementing integration tests for an ML pipeline in Databricks.
The current integration test runs the complete workflow but takes four hours to execute due to large dataset processing and extensive model training. They need to select an approach that will be the most effective for optimizing integration test execution while maintaining test reliability. The approach should also be based on MLOps best practices. Which approach will do this?

• A.Skip the model training phase entirely and only test data transformations and API endpoints using mock model predictions.
• B.Use small subsets of production-like data and reduce training iterations while maintaining the same pipeline structure and validation checkpoints in a staging environment that closely matches production.
• C.Run integration tests only in the production environment using full datasets to ensure complete accuracy of the testing process.
• D.Replace integration tests with unit tests for each pipeline component to reduce execution time and focus on individual component validation in a staging environment that closely matches production.

A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable.
They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df. Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

• A.
• B.
• C.
• D.It is not possible to deploy a scikit-learn model on a Spark DataFrame.
• E.