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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: C
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: A
"Data flows are available both in Azure Data Factory and Azure Synapse Pipelines" "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
Source: Adapted from community discussion — corebit
You have an Azure Data Factory instance that contains two pipelines named Pipeline1 and Pipeline2. Pipeline1 has the activities shown in the following exhibit.
Pipeline2 has the activities shown in the following exhibit.
You execute Pipeline2, and Stored procedure1 in Pipeline1 fails. What is the status of the pipeline runs?
Answer: A
Correct answer is A. The trick is the fact that pipeline 1 only has a Failure dependency between de activity's. In this situation this results in a Succeeded pipeline if the Stored procedure failed. If also the success connection was linked to a follow up activity, and the SP would fail, the pipeline would be indeed marked as failed. So A.
Source: Adapted from community discussion — SaferSephy
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: B
Answer should be B. An external table is based on a source flat file structure. It seems to make no sense to add additional date time columns to such a table.
Source: Adapted from community discussion — Canary_2021
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?
Answer: B
The job is to insert data from the files in container1 into Table1 (in the dedicated sql pool) and transform the data after that and we need to add a new additional column. External table are just references to the data, only metadata is really stored in the sql pool. Hence anything including external table will be not a solution. If you follow the different proposed solutions from previous questions, the most efficient solution is to use derived column transformation.
Source: Adapted from community discussion — emna2022
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?
Answer: B
Is part of a posible solution, but it isn't sufficient to meet the goal, yo need to pass the "Get metadata"'s output as a parameter to the ingest process, processing each file inside a "for" loop, for example. https://docs.microsoft.com/en-us/azure/data-factory/control-flow-get-metadata-activity
Source: Adapted from community discussion — juanlu46
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 Data Lake Storage account that contains a staging zone. You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics. Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that executes an Azure Databricks notebook, and then inserts the data into the data warehouse. Does this meet the goal?
Answer: A
I think is A. Yes. You can execute R code in a notebook, and then call it from Data Factory. You can check it at "Databricks Notebook activity" header: https://docs.microsoft.com/en-US/azure/data-factory/transform-data And also: https://docs.microsoft.com/en-us/azure/databricks/spark/latest/sparkr/overview
Source: Adapted from community discussion — juanlu46
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 Data Lake Storage account that contains a staging zone. You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics. Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that executes mapping data flow, and then inserts the data into the data warehouse. Does this meet the goal?
Answer: B
**VARIATIONS OF THIS QUESTION** Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that copies the data to a staging table in the data warehouse, and then uses a stored procedure to execute the R script. **NO** Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that executes an Azure Databricks notebook, and then inserts the data into the data warehouse. **YES** Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that executes mapping data flow, and then inserts the data into the data warehouse. **NO** Solution: You schedule an Azure Databricks job that executes an R notebook, and then inserts the data into the data warehouse. **YES**
Source: Adapted from community discussion — ExamDestroyer69
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 Data Lake Storage account that contains a staging zone. You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics. Solution: You schedule an Azure Databricks job that executes an R notebook, and then inserts the data into the data warehouse. Does this meet the goal?
Answer: A
The correct answer is "A. Yes" You can execute R code in a notebook, and then call it from Data Factory. You can check it at "Databricks Notebook activity" header: https://docs.microsoft.com/en-US/azure/data-factory/transform-data And also: https://docs.microsoft.com/en-us/azure/databricks/spark/latest/sparkr/overview
Source: Adapted from community discussion — juanlu46
You plan to create an Azure Data Factory pipeline that will include a mapping data flow. You have JSON data containing objects that have nested arrays. You need to transform the JSON-formatted data into a tabular dataset. The dataset must have one row for each item in the arrays. Which transformation method should you use in the mapping data flow?
Answer: D
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