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Microsoft 70-776 Exam

Microsoft 70-776 Exam

Title Exam Microsoft 70-776 Exam

Microsoft Perform Big Data Engineering on Microsoft Cloud Services (beta) Exam Updated Version 10.0

Product Type 91 Q&A with Explanations

1 of 1

Question: 1

Product Questions: 91

Version: 10.0

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 clustered column store index. Does this meet the goal?

  1. Yes
  2. No
Answer: 2

Question: 2

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?

  1. Yes
  2. No
Answer: 2

Question: 3

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 sections, 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 change the table to use a column that is not skewed for hash distribution. Does this meet the goal?

  1. Yes
  2. No
Answer: 1

Question: 4

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 a table named Table1 that contains 3 billion rows. Table1 contains data from the last 36 months.

At the end of every month, the oldest month of data is removed based on a column named DateTime.

You need to minimize how long it takes to remove the oldest month of data.

Solution: You specify DateTime as the hash distribution column. Does this meet the goal?

  1. Yes
  2. No
Answer: 2

Question: 5

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 a table named Table1 that contains 3 billion rows. Table1 contains data from the last 36 months.

At the end of every month, the oldest month of data is removed based on a column named DateTime.

You need to minimize how long it takes to remove the oldest month of data. Solution: You implement a columnstore index on the DateTime column. Does this meet the goal?

  1. Yes
  2. No
Answer: 1

Question: 6

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 a table named Table1 that contains 3 billion rows. Table1 contains data from the last 36 months.

At the end of every month, the oldest month of data is removed based on a column named DateTime.

You need to minimize how long it takes to remove the oldest month of data.

Solution: You implement round robin for table distribution. Does this meet the goal?

  1. Yes
  2. No
Answer: 2

Question: 7

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 troubleshooting a slice in Microsoft Azure Data Factory for a dataset that has been in a waiting state for the last three days. The dataset should have been ready two days ago.

The dataset is being produced outside the scope of Azure Data Factory. The dataset is defined by using the following JSON code.

You need to modify the JSON code to ensure that the dataset is marked as ready whenever there is data in the data store.

Solution: You change the external attribute to true. Does this meet the goal?

  1. Yes
  2. No

Answer: 1

8

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 troubleshooting a slice in Microsoft Azure Data Factory for a dataset that has been in a waiting state for the last three days. The dataset should have been ready two days ago.

The dataset is being produced outside the scope of Azure Data Factory. The dataset is defined by using the following JSON code.

You need to modify the JSON code to ensure that the dataset is marked as ready whenever there is data in the data store.

Solution: You change the interval to 24. Does this meet the goal?

  1. Yes
  2. No

Answer: 2

9

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 troubleshooting a slice in Microsoft Azure Data Factory for a dataset that has been in a waiting state for the last three days. The dataset should have been ready two days ago.

The dataset is being produced outside the scope of Azure Data Factory. The dataset is defined by using the following JSON code.

You need to modify the JSON code to ensure that the dataset is marked as ready whenever there is data in the data store.

Solution: You add a structure property to the dataset. Does this meet the goal?

  1. Yes
  2. No

Answer: 2

10

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 configure an activity to move data from blob storage to AzureDW. What should you create?

  1. a pipeline
  2. a linked service
  3. an automation runbook
  4. a dataset
Answer: 1

References: https://docs.microsoft.com/en-us/azure/data-factory/v1/data-factory-azure-blob- connector

Question: 11

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. End of repeated scenario.

You need to connect AzureDF to the storage account. What should you create?

  1. a gateway
  2. a dataset
  3. a linked service
  4. a pipeline
Answer: 3

References: https://docs.microsoft.com/en-us/azure/data-factory/v1/data-factory-azure-blob- connector

Question: 12

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.

End of repeated scenario.

You need to configure Azure Data Factory to connect to the on-premises SQL Server instance. What should you do first?

  1. Deploy an Azure virtual network gateway.
  2. Create a dataset in Azure Data Factory.
  3. From Azure Data Factory, define a data gateway.
  4. Deploy an Azure local network gateway.
Answer: 3

References: https://docs.microsoft.com/en-us/azure/data-factory/v1/data-factory-move-data- between-onpremand-cloud

Question: 13

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?

  1. a gateway
  2. a linked service
  3. a dataset
  4. a pipeline
Answer: 3

Question: 14

HOTSPOT

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.

How should you configure the storage to archive the source data? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Answer:

References: https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blob-storage- tiers

Question: 15

DRAG DROP

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.

Which three actions should you perform in sequence to migrate the on-premises data warehouse to Azure SQL Data Warehouse? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

References:

https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-load-from-

sqlserver-with-polybase

Question: 16

DRAG DROP

You plan to develop a solution for real-time sentiment analysis of Twitter data.

You need to create a Microsoft Azure Stream Analytics job query to count the number of tweets during a period.

Which Window function should you use for each requirement? To answer, drag the appropriate functions to the correct requirements. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

Answer:

References: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window- functions

 

 

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