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Answer: Leveraging Azure Machine Learning to periodically retrain data quality models, deploying these models as web services called by Databricks jobs for real-time quality checking
Option C is the most suitable proposition for designing and integrating an advanced testing framework using machine learning to predict and identify potential data quality issues in ETL pipelines deployed on Azure Databricks. Here‘s a detailed explanation of why this option is the best choice: 1. Leveraging Azure Machine Learning: Azure Machine Learning provides a comprehensive platform for building, training, and deploying machine learning models. By leveraging Azure Machine Learning, you can easily develop and train data quality models using historical data to predict and identify potential issues. 2. Periodically retraining data quality models: Data quality can change over time due to various factors such as changes in data sources or business requirements. By periodically retraining data quality models using Azure Machine Learning, you can ensure that the models remain accurate and up-to-date. 3. Deploying models as web services: Once the data quality models are trained and validated, you can deploy them as web services in Azure Machine Learning. These web services can be called by Databricks jobs for real-time quality checking, allowing you to identify and address data quality issues before they impact downstream systems. 4. Real-time quality checking: By integrating the deployed data quality models into Databricks jobs, you can perform real-time quality checking on incoming data batches. This proactive approach helps in identifying anomalies and potential issues early in the data processing pipeline, ensuring high data quality throughout the ETL process. Overall, leveraging Azure Machine Learning for training and deploying data quality models, and integrating them into Databricks jobs for real-time quality checking, provides a robust and efficient framework for ensuring the highest data quality in ETL pipelines deployed on Azure Databricks.
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To ensure the highest data quality in your ETL pipelines deployed on Azure Databricks, you decide to implement an advanced testing framework that uses machine learning to predict and identify potential data quality issues. How would you design and integrate this framework into your deployment process?
A
Incorporating an unsupervised learning model within Databricks notebooks that continuously learns from incoming data, flagging anomalies before they reach downstream systems
B
Using Databricks MLflow for model management, automating the deployment of data quality models into production pipelines, and monitoring model performance
C
Leveraging Azure Machine Learning to periodically retrain data quality models, deploying these models as web services called by Databricks jobs for real-time quality checking
D
Training a machine learning model on historical data quality issues and integrating it into the CI/CD pipeline to evaluate new data batches pre-deployment