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Answer: Combining two data sources into a single, comprehensive dataset
Databricks SQL is specifically designed for SQL-based querying, data analysis, and visualization tasks. Option C (Combining two data sources into a single, comprehensive dataset) is the correct choice because this is a core function of Databricks SQL - using SQL queries to join, union, or merge data from different sources into unified datasets for analysis. The community discussion with 100% consensus and 2 upvotes confirms this, noting that Databricks SQL excels at combining data from various formats and sources. The other options are less suitable: A (data quality testing) is typically handled by Databricks Data Quality or custom validation scripts; B (feature tracking) falls under MLflow or feature store management; D (customer segmentation) requires machine learning workflows better suited to Databricks Machine Learning; E (workflow automation) is handled by Databricks Workflows or Delta Live Tables.
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A data team has received multiple projects from a consultant that must be implemented in the Databricks Lakehouse Platform. Which of the following projects should be implemented using Databricks SQL?
A
Testing the quality of data as it is imported from a source
B
Tracking usage of feature variables for machine learning projects
C
Combining two data sources into a single, comprehensive dataset
D
Segmenting customers into like groups using a clustering algorithm
E
Automating complex notebook-based workflows with multiple tasks