You plan to build a structured streaming solution in Azure Databricks. The solution will count new events in five-minute intervals and report only events that arrive during the interval. The output will be sent to a Delta Lake table.Which output mode should you use?
Answer(s): C
Append Mode: Only new rows appended in the result table since the last trigger are written to external storage. This is applicable only for the queries where existing rows in the Result Table are not expected to change.Incorrect Answers:B: Complete Mode: The entire updated result table is written to external storage. It is up to the storage connector to decide how to handle the writing of the entire table.A: Update Mode: Only the rows that were updated in the result table since the last trigger are written to external storage. This is different from Complete Mode in that Update Mode outputs only the rows that have changed since the last trigger. If the query doesn?t contain aggregations, it is equivalent to Append mode.
https://docs.databricks.com/getting-started/spark/streaming.html
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1. You have files that are ingested and loaded into an Azure Data Lake Storage Gen2 container named container1.You plan to insert data from the files in container1 into Table1 and transform the data. Each row of data in the files will produce one row in the serving layer of Table1.You need to ensure that when the source data files are loaded to container1, the DateTime is stored as an additional column in Table1.Solution: In an Azure Synapse Analytics pipeline, you use a data flow that contains a Derived Column transformation. Does this meet the goal?
Answer(s): A
Use the derived column transformation to generate new columns in your data flow or to modify existing fields.
https://docs.microsoft.com/en-us/azure/data-factory/data-flow-derived-column
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1. You have files that are ingested and loaded into an Azure Data Lake Storage Gen2 container named container1.You plan to insert data from the files in container1 into Table1 and transform the data. Each row of data in the files will produce one row in the serving layer of Table1.You need to ensure that when the source data files are loaded to container1, the DateTime is stored as an additional column in Table1.Solution: You use a dedicated SQL pool to create an external table that has an additional DateTime column. Does this meet the goal?
Answer(s): B
Instead use the derived column transformation to generate new columns in your data flow or to modify existing fields.
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1. You have files that are ingested and loaded into an Azure Data Lake Storage Gen2 container named container1.You plan to insert data from the files in container1 into Table1 and transform the data. Each row of data in the files will produce one row in the serving layer of Table1.You need to ensure that when the source data files are loaded to container1, the DateTime is stored as an additional column in Table1.Solution: You use an Azure Synapse Analytics serverless SQL pool to create an external table that has an additional DateTime column.Does this meet the goal?
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1. You have files that are ingested and loaded into an Azure Data Lake Storage Gen2 container named container1.You plan to insert data from the files in container1 into Table1 and transform the data. Each row of data in the files will produce one row in the serving layer of Table1.You need to ensure that when the source data files are loaded to container1, the DateTime is stored as an additional column in Table1.Solution: In an Azure Synapse Analytics pipeline, you use a Get Metadata activity that retrieves the DateTime of the files. Does this meet the goal?
Instead use a serverless SQL pool to create an external table with the extra column.
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/create-use-external-tables
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