070-776試験無料問題集「Microsoft Engineering Data with Microsoft Cloud Services 認定」

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 are monitoring user queries to a Microsoft Azure SQL data warehouse that has six compute nodes.
You discover that compute node utilization is uneven. The rows_processed column from sys.dm_pdw_workers shows a significant variation in the number of rows being moved among the distributions for the same table for the same query.
You need to ensure that the load is distributed evenly across the compute nodes.
Solution: You add a nonclustered columnstore index.
Does this meet the goal?

You are building a Microsoft Azure Stream Analytics job definition that includes inputs, queries, and outputs.
You need to create a job that automatically provides the highest level of parallelism to the compute instances.
What should you do?

You plan to use Microsoft Azure Event Hubs to ingest sensor data. You plan to use Azure Stream Analytics to analyze the data in real time and to send the output directly to Azure Data Lake Store.
You need to write events to the Data Lake Store in batches.
What should you use?

You plan to create several U-SQLjobs.
You need to store structured data and code that can be shared by the U-SQL jobs.
What should you use?

You are designing a fact table that has 100 million rows and 1,800 partitions. The partitions are defined based on a column named OrderDayKey. The fact table will contain:
* Data from the last five years
* A clustered columnstore index
* A column named YearMonthKey that stores the year and the month
Multiple transformations will be performed on the fact table during the loading process. The fact table will be hash distributed on a column named OrderId.
You plan to load the data to a staging table and to perform transformations on the staging table. You will then load the data from the staging table to the final fact table.
You need to design a solution to load the data to the fact table. The solution must minimize how long it takes to perform the following tasks:
* Load the staging table.
* Transfer the data from the staging table to the fact table.
* Remove data that is older than five years.
* Query the data in the fact table
How should you configure the tables? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You are migrating an existing on-premises data warehouse named LocalDW to Microsoft Azure. You will use an Azure SQL data warehouse named AzureDW for data storage and an Azure Data Factory named AzureDF for extract transformation, and load (ETL) functions.
For each table in LocalDW, you create a table in AzureDW.

You have a Microsoft Azure SQL data warehouse. The following statements are used to define file formats in the data warehouse.

You have an external PolyBase table named file_factPowerMeasurement that uses the FileFormat_ORC file format.
You need to change file_ factPowerMeasurement to use the FileFormat_PARQUET file format.
Which two statements should you execute? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

You have a Microsoft Azure SQL data warehouse that has 10 compute nodes.
You need to export 10 TB of data from a data warehouse table to several new flat files in Azure Blob storage.
The solution must maximize the use of the available compute nodes.
What should you do?

Note: This question is part of a series of questions that present the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You are migrating an existing on-premises data warehouse named LocalDW to Microsoft Azure. You will use an Azure SQL data warehouse named AzureDW for data storage and an Azure Data Factory named AzureDF for extract, transformation, and load (ETL) functions.
For each table in LocalDW, you create a table in AzureDW.
On the on-premises network, you have a Data Management Gateway.
Some source data is stored in Azure Blob storage. Some source data is stored on an on-premises Microsoft SQL Server instance. The instance has a table named Table1.
After data is processed by using AzureDF, the data must be archived and accessible forever. The archived data must meet a Service Level Agreement (SLA) for availability of 99 percent. If an Azure region fails, the archived data must be available for reading always. The storage solution for the archived data must minimize costs.
End of repeated scenario.
You need to define the schema of Table1 in AzureDF.
What should you create?