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NEW QUESTION: 1
Your company has two identical servers. You have installed Windows Server 2003 Enterprise Edition on the first one and installed Exchange Server 2007 on it as well. You install Windows Server 2003 Enterprise on the second server, but you are unable to configure single copy clustering. What should you do?
A. Uninstall all roles other than the mailbox role on the first computer, then rerun the installation, select the role for Active Cluster Mailbox, then install the second node, select the Passive Cluster Mailbox Role, select Single Copy Cluster, and run the installation to completion
B. Select the Custom installation method on the second node, select the Passive Cluster Mailbox Role, select Single Copy Cluster, and run the installation to completion
C. Reformat and rebuild the first server so that it has just the operating system on it, configure clustering, and then install Exchange Server 2007 using SCC
D. Rerun the Exchange install on top of the existing server, select the role for Active Cluster Mailbox, install the second node, select the Passive Cluster Mailbox Role, then select Single Copy Cluster, and run the installation to completion.
Answer: C

NEW QUESTION: 2
An IP address is encapsulated at which of the following layers of the OSI model?
A. Network
B. Application
C. Transport
D. Data link
Answer: A

NEW QUESTION: 3
Ferris Plastics, Inc. is a medium sized company, with an enterprise network (access, distribution and core switches) that provides LAN connectivity from user PCs to corporate servers. The distribution switches are configured to use HSRP to provide a high availability solution.
* DSW1 -primary device for VLAN 101 VLAN 102 and VLAN 105
* DSW2 - primary device for VLAN 103 and VLAN 104
* A failure of GigabitEthemet1/0/1 on primary device should cause the primary device to release its status as the primary device, unless GigabitEthernet1/0/1 on backup device has also failed.
Troubleshooting has identified several issues. Currently all interfaces are up. Using the running configurations and show commands, you have been asked to investigate and respond to the following question.



During routine maintenance, it became necessary to shut down the GigabitEthernet1/0/1 interface on DSW1.
All other interfaces were up. During this time, DSW1 remained the active device for the VLAN 102 HSRP group. You have determined that there is an issue with the decrement value in the track command for the VLAN 102 HSRP group. What needs to be done to make the group function properly?
A. The decrement value on DSW1 should be greaterthan 5 and less than 15. 0
B. The decrement value on DSW1 should be greaterthan 9 and less than 15.
C. The decrement value on DSWTs should be greaterthan 195 and less than 205.
D. The decrement value on DSWTs should be greaterthan 190 and less than 200.
E. The decrement value on DSW1 should be greaterthan 11 and less than 19.
Answer: E
Explanation:
Explanation
The question clearly stated that there was an issue with the decrement value in VLAN 102 so we should check VLAN 102 on both DSW1 and DSW2 switches first. Click on the PC Console1 and PC Console2 to access these switches then use the "show running-config" command on both switches DSW1>enable DSW1#show running-config DSW2>enable DSW2#show running-config

As shown in the outputs, the DSW1's priority is 200 and is higher than that of DSW2 so DSW1 becomes active switch for the group. Notice that the interface Gig1/0/1 on DSW1 is being tracked so when this interface goes down, HSRP automatically reduces the router's priority by a congurable amount, in this case 5. Therefore the priority of DSW1 goes down from 200 to 195. But this value is still higher than that of DSW2 (190) so DSW1 remains the active switch for the group. To make DSW2 takes over this role, we have to configure DSW1's decrement value with a value equal or greater than 11 so that its result is smaller than that of DSW2 (200 - 11 < 190). Therefore C is the correct answer.

NEW QUESTION: 4
次の数値フィーチャを含むフィーチャセットがあります：X、Y、およびZ。
X、Y、およびZフィーチャのポアソン相関係数（r値）を次の図に示します。

ドロップダウンメニューを使用して、グラフィックに表示される情報に基づいて各質問に回答する回答選択肢を選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: 0.859122
Box 2: a positively linear relationship
+1 indicates a strong positive linear relationship
-1 indicates a strong negative linear correlation
0 denotes no linear relationship between the two variables.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear-correlation
Topic 1, Case Study
Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will be global and local market data to meet the following business goals:
* Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.
* Access a user's tendency to respond to an advertisement.
* Customize styles of ads served on mobile devices.
* Use video to detect penalty events.
Current environment
Requirements
* Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and snared using social media. The images and videos will have varying sizes and formats.
* The data available for model building comprises of seven years of sporting event media. The sporting event media includes: recorded videos, transcripts of radio commentary, and logs from related social media feeds feeds captured during the sporting events.
* Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats.
Advertisements
* Ad response models must be trained at the beginning of each event and applied during the sporting event.
* Market segmentation nxxlels must optimize for similar ad resporr.r history.
* Sampling must guarantee mutual and collective exclusivity local and global segmentation models that share the same features.
* Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
* Data scientists must be able to detect model degradation and decay.
* Ad response models must support non linear boundaries features.
* The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates from 0.1 +/-5%.
* The ad propensity model uses cost factors shown in the following diagram:

The ad propensity model uses proposed cost factors shown in the following diagram:

Performance curves of current and proposed cost factor scenarios are shown in the following diagram:

Penalty detection and sentiment
Findings
* Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection.
* Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
* Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
* Notebooks must execute with the same code on new Spark instances to recode only the source of the data.
* Global penalty detection models must be trained by using dynamic runtime graph computation during training.
* Local penalty detection models must be written by using BrainScript.
* Experiments for local crowd sentiment models must combine local penalty detection data.
* Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
* All shared features for local models are continuous variables.
* Shared features must use double precision. Subsequent layers must have aggregate running mean and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
* Ad response rates declined.
* Drops were not consistent across ad styles.
* The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
* Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models.
* All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too stow.
* Audio samples show that the length of a catch phrase varies between 25%-47%, depending on region.
* The performance of the global penalty detection models show lower variance but higher bias when comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.

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