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NEW QUESTION: 1
HOTSPOT


Answer:
Explanation:

NEW QUESTION: 2
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.

NEW QUESTION: 3
A network engineer wants to configure switch ports on Cisco Nexus 2000 and 2200 Fabric Extender switches that are connected to a Cisco Nexus 5500 Series Switch. Which three options allow this configuration? (Choose three.)
A. Connect to each of the Cisco Nexus 2200/2000 switches using Telnet, then configure the FEX switch ports.
B. Connect to a Cisco Nexus 5500 using Telnet, then configure the FEX switch ports.
C. Connect to a Cisco Nexus 5500 using a console cable, then configure the FEX switch ports.
D. Connect to a Cisco Nexus 5500 switch using SSH, then configure the FEX switch ports.
E. Connect to each of the Cisco Nexus 2200/2000 switches using SSH, then configure the FEX switch ports.
F. Connect to each of the Cisco Nexus 2200/2000 switches using a console cable, then configure the FEX switch ports.
Answer: B,C,D

NEW QUESTION: 4
Your network contains a Web server. The Web server is configured as shown in the following table.

Site1 fails intermittently.
You need to isolate Site1 from the other sites.
What should you do?
A. Add a managed handler to the Handler Mappings of Site1.
B. Upgrade the Site1 applications to use .NET Framework version 2.0.
C. Install and configure Windows System Resource Manager (WSRM).
D. Assign Site1 to a new application pool.
Answer: D
Explanation:
An application belongs to an application pool, which isolates the application from applications in other application pools on the server. In the case of managed code applications, make sure to associate your application together with an application pool that is running the .NET Framework version that your application requires.
http://technet.microsoft.com/en-us/library/cc771654.aspx