Databricks-Machine-Learning-Professional Exam Dumps : Databricks Certified Machine Learning Professional

To log feature importance data from a CSV file as an artifact of an MLflow run, the correct method is mlflow.log_artifact, which takes the local file path and the artifact path as arguments.The mlflow.log_model method is used to log a model with a specific flavor, such as pyfunc or spark. The mlflow.log_data method does not exist. The mlflow.log_model_and_data method also does not exist, and it is not necessary to log both the model and the data in this case. References:

• MLflow Tracking API
• MLflow Models

Batch deployment is the most common paradigm for machine learning projects, as it accounts for 80-90% of deployments1. Batch deployment leverages databases and object storage to process a large batch of input data at once, rather than processing each input data point individually in real time. Batch deployment is well suited for large data volume scenarios that need efficient processing but response time isn’t critical. For example, you might use batch deployment to generate recommendations for customers based on their purchase history, or to detect anomalies in sensor data collected over a period of time. Batch deployment is less expensive and more efficient than real-time deployment, as the underlying compute runs only during the batch job and can be scaled up or down as needed. Batch deployment can be implemented using Databricks Machine Learning endpoints, which provide a unified interface to invoke and manage model deployments across compute types2. References:

• Databricks Machine Learning Professional Exam Guide, Section 3: Model Deployment, p. 4
• Databricks Machine Learning Documentation, Deploying ML Models, Batch Inference, p. 1
• Machine Learning in Production - Databricks, The Four Deployment Paradigms, p. 9
• Deploy machine learning models in production environments, Deployment methods, p. 1

To drop a column from a Delta table, you need to enable column mapping mode for the table and use the ALTER TABLE statement with the DROP COLUMN clause. Alternatively, you can use the spark.read.format(“delta”).table(path) method to read the Delta table as a DataFrame and then use the drop method to drop the column. This will return a new DataFrame with the column removed. You can then overwrite the originalDelta table with the new DataFrame using the write.format(“delta”).mode(“overwrite”) method. The other options are incorrect because:

• Option A: spark.read.format(“delta”).load(path) will not read the Delta table as a table, but as a directory of files. This will not preserve the schema and metadata of the table, and will not allow you to overwrite the table with the new DataFrame.
• Option C: Delta tables can be modified using the Delta Lake APIs or SQL commands. Delta Lake supports schema evolution and column mapping to enable dropping columns from Delta tables.
• Option D: spark.read.table(path) will not work for Delta tables, as it expects a table name, not a path. To read a Delta table by its path, you need to use the spark.read.format(“delta”).load(path) or spark.read.format(“delta”).table(path) methods.
• Option E: spark.sql(“SELECT * EXCEPT star_rating FROM path”) will not drop the column from the Delta table, but only return a new DataFrame with the column excluded. This will not affect the original Delta table, and will not allow you to overwrite the table with the new DataFrame. References: Drop columns, Delta Lake Quickstart, Delta Lake column mapping