Databricks Updated Databricks-Certified-Data-Engineer-Associate Exam Questions and Answers by justin

One of the features of DLT is that it provides data quality metrics for each dataset in the pipeline, such as the number of records that pass or fail expectations, the number of records that are dropped, and the number of records that are written to the target. These metrics can be accessed from the DLT pipeline page, where the data engineer can click on each table and view the data quality statistics for the latest update or any previous update. This way, they can identify which table is dropping the records and why. References:

Monitor Delta Live Tables pipelines

Manage data quality with Delta Live Tables

File notification mode is the recommended option for scalable file discovery. On the stream’s first execution, Auto Loader performs a complete directory listing to discover existing files and establish a valid read position in its checkpoint and file-events cache. It can therefore ingest the 50,000 historical files without requiring a separate directory-listing stream. Subsequent executions discover newly arriving files through cloud-storage events instead of repeatedly scanning the entire directory, improving discovery performance and reducing cloud API and compute costs. Auto Loader maintains its checkpoint state and exactly-once processing guarantees when using either detection mode. Although switching modes across restarts is supported, it is unnecessary for this requirement. Options B, C, and D incorrectly imply that notification mode cannot efficiently initialize from existing files. Therefore, option A is correct.

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Auto Loader in Databricks utilizes Spark Structured Streaming for processing data incrementally. This allows Auto Loader to efficiently ingest streaming or batch data at scale and to recognize new data as it arrives in cloud storage. Spark Structured Streaming provides the underlying engine that supports various incremental data loading capabilities like schema inference and file notification mode, which are crucial for the dynamic nature of data lakes.