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NEW QUESTION: 1
You are performing feature engineering on a dataset.
You must add a feature named CityName and populate the column value with the text London.
You need to add the new feature to the dataset.
Which Azure Machine Learning Studio module should you use?
A. Latent Dirichlet Allocation
B. Edit Metadata
C. Execute Python Script
D. Preprocess Text
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Typical metadata changes might include marking columns as features.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/edit-metadata Testlet 1 Case study Overview You are a data scientist in a company that provides data science for professional sporting events. Models will use 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.

Assess 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
Media used for penalty event detection will be provided by consumer devices. Media may include

images and videos captured during the sporting event and shared 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 video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo

formats.
Penalty detection and sentiment
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.
Advertisements
During the initial weeks in production, the following was observed:
Ad response rated 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 uncorrelated features.
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 slow.
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 shows 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.
Ad response models must be trained at the beginning of each event and applied during the sporting

event.
Market segmentation models must optimize for similar ad response history.

Sampling must guarantee mutual and collective exclusively between 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.
Ad response models must support non-linear boundaries of features.

The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated

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:

NEW QUESTION: 2
You administer a Microsoft SQL Server 2012 database.
You configure Transparent Data Encryption (TDE) on the Orders database by using the following statements:
CREATE MASTER KEY ENCRYPTION BY PASSWORD = 'MyPassword1!'
CREATE CERTIFICATE TDE_Certificate WITH SUBJECT = 'TDE Certificate';
BACKUP CERTIFICATE TDE_Certificate TO FILE = ''d:\TDE_Certificate.cer'
WITH PRIVATE KEY (FILE = 'D:\TDE_Certificate.key',
ENCRYPTION BY PASSWORD = 'MyPassword1!');
CREATE DATABASE ENCRYPTION KEY
WITH ALGORITHM = AES_256
ENCRYPTION BY SERVER CERTIFICATE TDE_Certificate;
ALTER DATABASE Orders SET ENCRYPTION ON;
You attempt to restore the Orders database and the restore fails. You copy the encryption file to the original location.
A hardware failure occurs and so a new server must be installed and configured.
After installing SQL Server to the new server, you restore the Orders database and copy the encryption files to their original location. However, you are unable to access the database.
You need to be able to restore the database.
Which Transact-SQL statement should you use before attempting the restore?
ALTER DATABASE Master SET ENCRYPTION OFF;
A. CREATE CERTIFICATE TDE_Certificate
B. FROM FILE = 'd:\TDE_Certificate.cer';
C. WITH SUBJECT = 'TDE Certificate'; USE Orders;
CREATE DATABASE ENCRYPTION KEY
WITH ALGORITHM = AES_256
ENCRYPTION BY SERVER CERTIFICATE TDE_Certificate;
CREATE CERTIFICATE TDE_Certificate
D. FROM FILE = 'd:\TDE_Certificate.cer'
WITH PRIVATE KEY (FILE = 'D:\TDE_Certificate.key',
DECRYPTION BY PASSWORD = 'MyPassword1!');
CREATE CERTIFICATE TDE_Certificate
Answer: D
Explanation:
Explanation/Reference:
Explanation:
The CREATE CERTIFICATE command adds a certificate to a database in SQL Server.
Creating a certificate from a file
The following example creates a certificate in the database, loading the key pair from files.
Code
Copy
USE AdventureWorks2012;
CREATE CERTIFICATE Shipping11
FROM FILE = 'c:\Shipping\Certs\Shipping11.cer'
WITH PRIVATE KEY (FILE = 'c:\Shipping\Certs\Shipping11.pvk',
DECRYPTION BY PASSWORD = 'sldkflk34et6gs%53#v00');
GO
References: https://docs.microsoft.com/en-us/sql/t-sql/statements/create-certificate-transact-sql

NEW QUESTION: 3
If the maturity of a straight 3-month deposit fails on a Saturday, which is also the last day of the month, what will be the final end date of this transaction?
A. Saturday
B. The following Monday
C. The last business day of the month
D. The first business day of the following month
Answer: C

NEW QUESTION: 4
ネットワークには、DC1という名前のドメインコントローラを含むcontoso.comという名前のActive Directoryドメインが含まれています。 ネットワークのすべてのDNSサーバーはBIND 10を実行します。
境界ネットワークには、Windows Server 2016を実行するDHCP1という名前のDHCPサーバーが含まれています。DHCP1は、WORKGROUPという名前のワークグループのメンバーです。 DHCP1は、Wi-FiネットワークにアクセスするゲストにIPアドレスリースを提供します。
複数のエンジニアが、Windows Server 2016を実行するリモートアクセスサーバーへのVPN接続を使用して、リモートからネットワークにアクセスします。すべてのVPN接続は証明書ベースの認証を使用し、ネットワークポリシーサーバー（NPS）のアクセスポリシーの対象です。 証明書はCA1というエンタープライズ証明機関（CA）によって発行されます。
ネットワーク上のすべてのWindowsコンピュータは、Key Management Service（KMS）を使用してアクティブ化されます。 オンプレミスユーザーは、リモートデスクトップサービス（RDS）を使用します。
IPアドレス管理（IPAM）をネットワークに展開する予定です。
IPAMを使用してネットワーク上で実行できるアクションはどれですか？
A. DHCP1でDHCP予約を作成します。
B. KMSサーバー上のアクティベーションを管理します。
C. CA1に対する証明書登録要求を監査する。
D. DC1からの認証イベントを監査します。
Answer: D