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NEW QUESTION: 1
You have a Microsoft Azure SQL data warehouse that has a fact table named FactOrder.
FactOrder contains three columns named Customer Id. Order Id, and Order DateKey.
FactOrder is hash distributed on Customerld. Order Id is the unique identifier for FactOrder.
FactOrder contains 3 million rows.
Orders are distributed evenly among different customers from a table named dimCustomers that contains 2 million rows.
You often run queries that join FactOrder and dimCustomers by selecting and groupinq by the OrderDateKey column.
You add 7 million rows to FactOrder. Most of the new records have a more recent
OrderDateKey value than the previous records.
You need to reduce the execution time of queries that group on Order DateKey and that join dimCustomers and FactOrder.
What should you do?
A. Change the distribution for the dimCustomers table to OrderDateKey.
B. Change the distribution tor the FactOrder table to be based on Orderld.
C. Change the distribution foe the FactOrder table to round robin
D. Update the statistics for the OrderDateKey column.
Answer: A

NEW QUESTION: 2
What should you do to improve high availability of the real-time data processing solution?
A. Deploy a High Concurrency Databricks cluster.
B. Set Data Lake Storage to use geo-redundant storage (GRS).
C. Deploy identical Azure Stream Analytics jobs to paired regions in Azure.
D. Deploy an Azure Stream Analytics job and use an Azure Automation runbook to check the status of the job and to start the job if it stops.
Answer: C
Explanation:
Guarantee Stream Analytics job reliability during service updates
Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can have a service update deploy on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break due to the introduction of a bug. If you are running mission critical jobs, these risks need to be avoided. You can reduce this risk by following Azure's paired region model.
Scenario: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure

NEW QUESTION: 3
Which statement best applies to the following schema?

A. A 1:N relationship to the cus:company schema is declared
B. A 1:1 relationship to the cus:recipient schema is declared
C. A 1:1 relationship to the cus:company schema is declared
D. A 1:N relationship to the cus:recipient schema is declared
Answer: B

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