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NEW QUESTION: 1
Sie erstellen eine Azure-Webanwendung mit dem Namen WebApp1. WebApp1 verfügt über die in der folgenden Abbildung gezeigten Einstellungen für die automatische Skalierung.
Die Regeln für das Ausblenden und Einblenden sind so konfiguriert, dass sie eine Dauer von 10 Minuten und eine Abkühlzeit von fünf Minuten haben.
Verwenden Sie die Dropdown-Menüs, um die Antwortauswahl auszuwählen, die jede Anweisung basierend auf den in der Grafik dargestellten Informationen vervollständigt.
HINWEIS: Jede richtige Auswahl ist einen Punkt wert.
Answer:
Explanation:
Explanation
NEW QUESTION: 2
An engineer needs to install an SC8000 with four FC ports in each controller and two FC switches set up in a dual fabric. Each controller also has two 10 Gb iSCSI ports. All iSCSI ports are connected to an ethernet switch. The system uses virtual ports.
How many fault domains are required?
A. 0
B. 1
C. 2
D. 3
Answer: C
NEW QUESTION: 3
Drag and drop the components from the left onto the descriptions on the right that indicate what occurs to Fibre Channel fabric when that component fails.
Answer:
Explanation:
NEW 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 are using Azure Machine Learning Studio to perform feature engineering on a dataset.
You need to normalize values to produce a feature column grouped into bins.
Solution: Apply an Entropy Minimum Description Length (MDL) binning mode.
Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Entropy MDL binning mode: This method requires that you select the column you want to predict and the column or columns that you want to group into bins. It then makes a pass over the data and attempts to determine the number of bins that minimizes the entropy. In other words, it chooses a number of bins that allows the data column to best predict the target column. It then returns the bin number associated with each row of your data in a column named <colname>quantized.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins