You have a fact table named PowerUsage that has 10 billion rows. PowerUsage contains data about customer power usage during the last 12 months. The usage data is collected every minute. PowerUsage contains the columns configured as shown in the following table.

LocationNumber has a default value of 1. The MinuteOfMonth column contains the relative minute within each month. The value resets at the beginning of each month.
A sample of the fact table data is shown in the following table.

There is a related table named Customer that joins to the PowerUsage table on the CustomerId column. Sixty percent of the rows in PowerUsage are associated to less than 10 percent of the rows in Customer. Most queries do not require the use of the Customer table. Many queries select on a specific month.
You need to minimize how long it takes to find the records for a specific month.
What should you do?

• A. Implement partitioning by using the MinuteOfMonth column. Implement hash distribution by using the MeasurementId column.
• B. Implement partitioning by using the MonthKey column. Implement hash distribution by using the MeasurementId column.
• C. Implement partitioning by using the CustomerId column. Implement hash distribution by using the MonthKey column.
• D. Implement partitioning by using the MonthKey column. Implement hash distribution by using the CustomerId 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 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?

• A. No
• B. Yes

You use Microsoft Visual Studio to develop custom solutions for customers who use Microsoft Azure Data Lake Analytics.
You install the Data Lake Tools for Visual Studio.
You need to identify which tasks can be performed from Visual Studio and which tasks can be performed from the Azure portal.
What should you identify for each task? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

You have the following process:
* A CSV file is ingested by Microsoft Azure Stream Analytics.
* Scoring is performed by Azure Machine Learning.
* Stream Analytics returns sentiment scoring through a web service endpoint.
* Stream Analytics creates an output blob.
You need to view the output of the scoring operation and to evaluate the throughput to the Machine Learning models.
Which monitoring data should you evaluate from the Azure portal?

• A. the request count of Machine Learning
• B. the event count of Stream Analytics
• C. the request count of Stream Analytics
• D. the event count of Machine Learning