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NEW QUESTION: 1
You work as a Software Developer for ABC Inc. The company uses Visual Studio .NET as its application development platform. You create the definition for a Car class by using the following code segment:
public class Car {
[XmlAttribute(AttributeName = "category")]
public string CarType;
public string model;
[XmlIgnore]
public int year;
[XmlElement(ElementName = "mileage")]
public int miles;
public ConditionType condition;
public Car() {
}
public enum ConditionType {
[XmlEnum("Poor")] BelowAverage,
[XmlEnum("Good")] Average,
[XmlEnum("Excellent")] AboveAverage
}}
You create an instance of the Car class. You fill the public fields of the Car class as shown in the
table below:
You are required to recognize the XML block that is produced by the Car class after serialization.
Which of the following XML blocks represents the output of serializing the Car class?
A. <?xml version="1.0" encoding="utf-8"?>
<Car
xmlns:xsi="http://www.w3.org/2002/XMLSchema-instance"
xmlns:xsd="http://www.w3.org/2002/XMLSchema"
category="sedan">
<model>racer</model>
<mileage>15000</mileage>
<conditionType>Excellent</conditionType>
</Car>
B. <?xml version="1.0" encoding="utf-8"?>
<Car
xmlns:xsi="http://www.w3.org/2002/XMLSchema-instance"
xmlns:xsd="http://www.w3.org/2002/XMLSchema""
CarType="sedan">
<model>racer</model>
<miles>15000</miles>
<condition>AboveAverage</condition>
</Car>
C. <?xml version="1.0" encoding="utf-8"?>
<Car
xmlns:xsi="http://www.w3.org/2002/XMLSchema-instance"
xmlns:xsd="http://www.w3.org/2002/XMLSchema">
<category>sedan</category>
<model>racer</model>
<mileage>15000</mileage>
<condition>Excellent</condition>
</Car>
D. <?xml version="1.0" encoding="utf-8"?>
<Car
xmlns:xsi="http://www.w3.org/2002/XMLSchema-instance"
xmlns:xsd="http://www.w3.org/2002/XMLSchema"
category="sedan">
<model>racer</model>
<mileage>15000</mileage>
<condition>Excellent</condition>
</Car>
Answer: D

NEW QUESTION: 2
TextAnalyticsサービスを使用するソリューションを開発しています。
ドキュメントのコレクションの主な論点を特定する必要があります。
どのタイプの自然言語処理を使用する必要がありますか？
A. キーフレーズ抽出
B. 感情分析
C. 言語検出
D. エンティティの認識
Answer: A
Explanation:
Explanation
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

NEW QUESTION: 3
매출 채권 (AR) 프로세스를 감사하는 동안 내부 감사인은 AR 직원이 여전히 정기적으로 조정을 수행하지 않으며 이전 감사에 따라 권고 된 사항을 언급했습니다. 재무 보고 기능에 의한 모니터링이 단점을 감지하지 못했습니다. 재무보고 기능과 AR은 모두 조치 계획을 구현하는 컨트롤러에 보고합니다. 다음 중 두 가지 관찰을 하나의보고 된 결과로 결합하려는 내부 감사인의 결정을 뒷받침하는 것은 무엇입니까?
A. 관측치는 공통의 통제를 가지며 사전 감사에서 언급되었다.
B. 동일한 감사 중에 관찰되었으며 조치 계획에는 공통 소유자가 있습니다.
C. 관찰에는 공통 프로세스가 있으며 관찰에 대한 실행 계획에는 공통 소유자가 있습니다.
D. 관찰은 공통 프로세스 내에서 동일한 제어 활동과 관련이 있습니다.
Answer: C

NEW QUESTION: 4
以下の各ステートメントについて、そのステートメントが正しい場合は「はい」を選択してください。そうでなければ、いいえを選択します。
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: No
Not all Azure regions support availability zones.
Box 2: No
Regions that support availability zones support Linux virtual machines.
Box 3: Yes
Availability Zones is a high-availability offering that protects your applications and data from datacenter failures. Availability Zones are unique physical locations within an Azure region. Each zone is made up of one or more datacenters equipped with independent power, cooling, and networking. To ensure resiliency, there's a minimum of three separate zones in all enabled regions. The physical separation of Availability Zones within a region protects applications and data from datacenter failures. Zone-redundant services replicate your applications and data across Availability Zones to protect from single-points-of-failure. With Availability Zones, Azure offers industry best 99.99% VM uptime SLA.
References:
https://docs.microsoft.com/en-gb/azure/availability-zones/az-overview