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NEW QUESTION: 1
You plan to deploy Exchange Server 2016 to a new server. The server will host 1,000 mailboxes.
You need to identify whether the new server has the required disk throughput and speed.
Which two tools should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. LoadGen
B. Jetstress
C. Isinteg
D. the Exchange Server Best Practices Analyzer Tool
E. the Exchange Server Role Requirements Calculator
Answer: A,B
Explanation:
Explanation
Explanation
To determine how many Exchange servers are required to manage user load, use the following capacity planning tools:
Microsoft Exchange Jetstress
Microsoft Exchange Load Generator
Note:
The Jetstress tool is designed to simulate Exchange I/O at the database level by interacting directly with the database technology of the Extensible Storage Engine (ESE), also known as Jet, that Exchange is built on.
Jetstress can be configured to test the maximum I/O throughput available to your disk subsystem within the required performance constraints of Exchange, or it can accept a desired profile of user count and I/Os per second per user and validate that the disk subsystem is capable of maintaining an acceptable level of performance with such a profile. Jetstress testing can be used to validate storage reliability and performance prior to the deployment of Exchange servers. You should run Jetstress if you are concerned about your storage subsystem performance or if you need to determine a system's I/O capacity.
The Load Generator (LoadGen) tool is designed to produce a simulated client workload against a test Exchange deployment. This workload can be used to evaluate how Exchange performs, and can also be used to analyze the effect of various configuration changes on Exchange behavior and performance while the system is under load Use the output from these tests in the following ways:
Validate deployments
Calculate the client computer response time for the server configuration under client load Estimate the number of users per server Identify bottlenecks on the server References: https://technet.microsoft.com/en-us/library/dd335108(v=exchg.141).aspx

NEW QUESTION: 2
HOTSPOT


Answer:
Explanation:

NEW QUESTION: 3
-- Exhibit --
user@host> request services application-identification application copy junos:AIM-HTTP-API error: Cannot commit to junos configure DB.
----------------------------------------------------------------------
could not lock modified database
mgd xcommit failed
Copy application junos:AIM-HTTP-API failed.
-- Exhibit --
Click the Exhibit button.
You want to make a custom copy of the junos: AIM-HTTP-API application signature. However, when you attempt to copy the application signature, you receive the error shown in the exhibit.
What is causing the problem?
A. The AppID signature database is corrupt.
B. You cannot copy nested applications.
C. The candidate configuration is different from the active configuration.
D. The my:AIM-HTTP-API application signature already exists in the active configuration.
Answer: C

NEW QUESTION: 4
You train a classification model by using a decision tree algorithm.
You create an estimator by running the following Python code. The variable feature_names is a list of all feature names, and class_names is a list of all class names.
from interpret.ext.blackbox import TabularExplainer

You need to explain the predictions made by the model for all classes by determining the importance of all features.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: Yes
TabularExplainer calls one of the three SHAP explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer).
Box 2: Yes
To make your explanations and visualizations more informative, you can choose to pass in feature names and output class names if doing classification.
Box 3: No
TabularExplainer automatically selects the most appropriate one for your use case, but you can call each of its three underlying explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer) directly.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-aml