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NEW QUESTION: 1
AzureでホストされているLinux仮想マシン（VM）に新しいアプリケーションをデプロイする予定です。
業界標準の暗号化テクノロジを使用して組織全体のセキュリティとコンプライアンスの要件を満たすことで、VM全体を安全に保護する必要があります。
VM用にAzure Disk Encryptionを構成する必要があります。
Azure Cliコマンドをどのように完成させるべきですか？回答するには、回答領域で適切なオプションを選択します。
注：それぞれ正しい選択は1ポイントの価値があります。

Answer:
Explanation:

Explanation


Box 1: keyvault
Create an Azure Key Vault with az keyvault create and enable the Key Vault for use with disk encryption.
Specify a unique Key Vault name for keyvault_name as follows:
keyvault_name=myvaultname$RANDOM
az keyvault create \
--name $keyvault_name \
--resource-group $resourcegroup \
--location eastus \
--enabled-for-disk-encryption True
Box 2: keyvault key
The Azure platform needs to be granted access to request the cryptographic keys when the VM boots to
decrypt the virtual disks. Create a cryptographic key in your Key Vault with az keyvault key create. The
following example creates a key named myKey:
az keyvault key create \
--vault-name $keyvault_name \
--name myKey \
--protection software
Box 3: vm
Create a VM with az vm create. Only certain marketplace images support disk encryption. The following
example creates a VM named myVM using an Ubuntu 16.04 LTS image:
az vm create \
--resource-group $resourcegroup \
--name myVM \
--image Canonical:UbuntuServer:16.04-LTS:latest \
--admin-username azureuser \
--generate-ssh-keys \
Box 4: vm encryption
Encrypt your VM with az vm encryption enable:
az vm encryption enable \
--resource-group $resourcegroup \
--name myVM \
--disk-encryption-keyvault $keyvault_name \
--key-encryption-key myKey \
--volume-type all
Note: seems to an error in the question. Should have enable instead of create.
Box 5: all
Encrypt both data and operating system.
References:
https://docs.microsoft.com/bs-latn-ba/azure/virtual-machines/linux/encrypt-disks

NEW QUESTION: 2
Overview
Application Overview
Contoso, Ltd., is the developer of an enterprise resource planning (ERP) application.
Contoso is designing a new version of the ERP application. The previous version of the ERP application used SQL Server 2008 R2.
The new version will use SQL Server 2014.
The ERP application relies on an import process to load supplier data. The import process updates thousands of rows simultaneously, requires exclusive access to the database, and runs daily.
You receive several support calls reporting unexpected behavior in the ERP application. After analyzing the calls, you conclude that users made changes directly to the tables in the database.
Tables
The current database schema contains a table named OrderDetails.
The OrderDetails table contains information about the items sold for each purchase order. OrderDetails stores the product ID, quantities, and discounts applied to each product in a purchase order.
The product price is stored in a table named Products. The Products table was defined by using the SQL_Latin1_General_CP1_CI_AS collation.
A column named ProductName was created by using the varchar data type. The database contains a table named Orders.
Orders contains all of the purchase orders from the last 12 months. Purchase orders that are older than
12 months are stored in a table named OrdersOld.
The previous version of the ERP application relied on table-level security.
Stored Procedures
The current version of the database contains stored procedures that change two tables. The following shows the relevant portions of the two stored procedures:

Customer Problems
Installation Issues
The current version of the ERP application requires that several SQL Server logins be set up to function correctly. Most customers set up the ERP application in multiple locations and must create logins multiple times.
Index Fragmentation Issues
Customers discover that clustered indexes often are fragmented. To resolve this issue, the customers defragment the indexes more frequently. All of the tables affected by fragmentation have the following columns that are used as the clustered index key:

Backup Issues
Customers who have large amounts of historical purchase order data report that backup time is unacceptable.
Search Issues
Users report that when they search product names, the search results exclude product names that contain accents, unless the search string includes the accent.
Missing Data Issues
Customers report that when they make a price change in the Products table, they cannot retrieve the price that the item was sold for in previous orders.
Query Performance Issues
Customers report that query performance degrades very quickly. Additionally, the customers report that users cannot run queries when SQL Server runs maintenance tasks. Import Issues During the monthly import process, database administrators receive many supports call from users who report that they cannot access the supplier data. The database administrators want to reduce the amount of time required to import the data.
Design Requirements
File Storage Requirements
The ERP database stores scanned documents that are larger than 2 MB. These files must only be accessed through the ERP application. File access must have the best possible read and write performance.
Data Recovery Requirements
If the import process fails, the database must be returned to its prior state immediately.
Security Requirements
You must provide users with the ability to execute functions within the ERP application, without having direct access to the underlying tables.
Concurrency Requirements
You must reduce the likelihood of deadlocks occurring when Sales.Prod and Sales.Proc2 execute.
You need to recommend a solution that addresses the concurrency requirement. What should you recommend?
A. Modify the stored procedures to update tables in the same order for all of the stored procedures.
B. Break each stored procedure into two separate procedures, one that changes Sales.Table1 and one that changes Sales.Table2.
C. Call the stored procedures in a Distributed Transaction Coordinator (DTC) transaction.
D. Make calls to Sales.Proc1 and Sales.Proc2 synchronously.
Answer: A
Explanation:
Explanation/Reference:
Explanation:
- Concurrency Requirements
You must reduce the likelihood of deadlocks occurring when Sales.Proc1 and Sales.Proc2 execute.

NEW QUESTION: 3
イベント中のコール数を推定する回帰モデルを構築しています。
特徴値がポアソン回帰モデルを構築する条件を満たしているかどうかを判断する必要があります。
機能セットに含める必要がある2つの条件はどれですか？私は正しい答えが解決策の一部を提示します。注意：
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。
A. データは整数でなければなりません。
B. ラベルデータは非離散でなければなりません。
C. ラベルデータは正でも負でもかまいませんが、
D. ラベルデータは負の値でなければなりません。
E. ラベルデータは正の値である必要があります
Answer: A,E
Explanation:
Explanation
Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts. Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the following conditions:
* The response variable has a Poisson distribution.
* Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.
* A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with non-whole numbers.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson-regression
Topic 2, Case Study 1
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: 4
A network technician is concerned that a user is utilizing a company PC for file sharing and using a large amount of the bandwidth. Which of the following tools can be used to identify the IP and MAC address of the user's PC?
A. System log
B. History log
C. Network sniffer
D. Nslookup
Answer: C
Explanation:
Network sniffer is a computer program or a piece of computer hardware that can intercept and log traffic passing over a digital network or part of a network. As data streams flow across the network, the sniffer captures each packet and, if needed, decodes the packet's rawdata, showing the values of various fields in the packet, and analyzes its content according to the appropriate RFC or other specifications.