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NEW QUESTION: 1
You have a server named Server1 that runs Windows Server 2012 R2. Server1 has the File Server Resource Manager role service installed.
Each time a user receives an access-denied message after attempting to access a folder on Server1, an email notification is sent to a distribution list named DL1.
You create a folder named Folder1 on Server1, and then you configure custom NTFS permissions for Folder1.
You need to ensure that when a user receives an access-denied message while attempting to access Folder1, an email notification is sent to a distribution list named DL2. The solution must not prevent DL1 from receiving notifications about other access-denied messages.
What should you do?
A. From Server Manager, run the New Share Wizard to create a share for Folder1 by selecting the SMB Share - Advanced option.
B. From the File Server Resource Manager console, modify the Email Notifications settings.
C. From the File Server Resource Manager console, modify the Access-Denied Assistance settings.
D. From Server Manager, run the New Share Wizard to create a share for Folder1 by selecting the SMB Share -Applications option.
Answer: B
Explanation:
Configure the email notification settings You must configure the email notification settings on each file server that will send the access-denied assistance messages.
Open File Server Resource Manager. In Server Manager, click Tools, and then
click File Server Resource Manager.
Right-click File Server Resource Manager (Local), and then click Configure
Options.
--
Click the Email Notifications tab.
Configure the following settings:
Click Send Test E-mail to ensure that the email notifications are configured
correctly.
Click OK.
-
---
NEW QUESTION: 2
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?
A. Weight
B. Streaming
C. Cosine
D. Batch
Answer: D
Explanation:
Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper.
In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript." Scenario:
Local penalty detection models must be written by using BrainScript.
References:
https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics
Topic 1, One 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
Click on the Exhibit Button to view the Formula Sheet. What is the ISO code for the currency of Hungary?
A. HRN
B. HKD
C. HUG
D. HUF
Answer: D
NEW QUESTION: 4
회사가 온 프레미스 데이터베이스를 AWS로 마이그레이션하고 있습니다. 이 회사의 백엔드 응용 프로그램은 보고 목적으로 많은 양의 데이터베이스 쿼리를 생성하며이 읽기 중 일부를 읽기 전용 복제본으로 오프로드하여 기본 데이터베이스가 계속 효율적으로 수행되도록 하려고 합니다.
어떤 AWS 데이터베이스 플랫폼이 이를 달성합니까? (2 개 선택)
A. Microsoft SQL Server 용 Amazon RDS
B. Oracle 용 Amazon RDS
C. Amazon DynamoDB
D. PostgreSQL 용 Amazon RDS
E. MariaDB 용 Amazon RDS
Answer: D,E
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