Databricks Databricks-Machine-Learning-Professional Practice Test Questions Answers

To replace all data in a feature table with newly computed data, you can use the fs.write_table method with the mode argument set to “overwrite”. This will overwrite the existing data in the feature table with the data in the provided DataFrame. The other options are incorrect because:

Option A: fs.create_table will create a new feature table, not replace the data in an existing one. If the feature table already exists, it will raise an exception.

Option B: fs.write_table without specifying the mode argument will use the default mode of “merge”, which will merge the new data with the existing data, not replace it.

Option C: fs.write table is not a valid method name, as it is missing an underscore. Also, the mode argument of “merge” will not replace the data, but merge it with the existing data.

Option E: fs.create_table with the mode argument of “merge” is not a valid combination, as the mode argument is only applicable for fs.write_table. Also, the mode argument of “merge” will not replace the data, but merge it with the existing data. References: Databricks FeatureStoreClient, Work with features in Workspace Feature Store

The fs.read_table operation can be used to return a Spark DataFrame of a data set associated with a Feature Store table. This operation takes the name of the Feature Store table and an optional time travel specification as arguments. The fs.create_table operation is used to create a new Feature Store table from a Spark DataFrame. The fs.write_table operation is used to write data to an existing Feature Store table. The fs.get_table operation is used to get the metadata of a Feature Store table, not the data itself. There is a way to accomplish this task with fs, so option D is incorrect. References:

Feature Store Client

Feature Store Tables

Real-time deployment is a type of model deployment where predictions are computed on-demand at query time. This can be useful when the feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time. Real-time deployment can leverage Databricks Model Serving, which allows users to host MLflow models as REST endpoints on Databricks1. The other options are incorrect because they either do not meet the requirements or are not supported by Databricks Machine Learning. Edge/on-device deployment is a type of model deployment where predictions are computed on the edge device, such as a smartphone or a sensor. This can be useful when there is limited or no network connectivity, or when privacy and security are important. However, edge deployment is not supported by Databricks Machine Learning2. Streaming deployment is a type of model deployment where predictions are computed on streaming data, such as sensor readings or web logs. This can be useful when the data volume is high and the results need to be updated continuously. However, streaming deployment is not supported by Databricks Model Serving1. Batch deployment is a type of model deployment where predictions are computed in advance and stored for later use. This can be useful when the feature values are known ahead of time and the predictions do not need to be delivered quickly. However, batch deployment does not meet the requirements of this scenario, as the feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time. References: MLflow Model Serving, Model deployment patterns

A two-way Chi-squared test is a statistical test that can be used to compare the frequencies of categorical variables across two or more groups. In this case, the engineer can use a two-way Chi-squared test to compare the frequencies of the categorical input variable values across different time periods (e.g., months, quarters, etc.) and see if there is a significant difference in the proportion of missing values. A significant difference would indicate that the missing values are not randomly distributed and may affect the performance of the machine learning model. References:

Databricks Machine Learning Professional Exam Guide, Section 4: Solution and Data Monitoring, p. 5

Databricks Machine Learning Documentation, Monitoring ML Models, Data Drift Detection, p. 3

MLflow provides a module called mlflow.shap that can be used to automatically calculate and log a Shapley feature importance plot using the SHAP library. The mlflow.shap.log_explanation function takes a predict function and a sample of input data, and computes the SHAP values and base values for each feature. It then logs the following artifacts to the current run:

Base values

SHAP values (computed using shap.KernelExplainer)

Summary bar plot (shows the average impact of each feature on model output) The mlflow.shap.log_explanation function can be used with any model that has a predict function, such as scikit-learn, xgboost, etc1234 References:

mlflow.shap — MLflow 2.9.1 documentation

Extract feature importance from a mlflow 1.9 PyFuncModel model

9.6 SHAP (SHapley Additive exPlanations) | Interpretable Machine Learning

load MLFlow model and plot feature importance with feature names