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NEW QUESTION: 1
Sie überprüfen die Konfiguration eines Azure Search-Indexers.
Der Dienst wurde mit einem Indexer konfiguriert, der die Option Daten importieren verwendet. Der Index wird mithilfe von Optionen konfiguriert, die in der Ausstellung Index Configuration (Indexkonfiguration) gezeigt werden. (Klicken Sie auf die Registerkarte Indexkonfiguration.)
Sie verwenden eine Azure-Tabelle als Datenquelle für den Importvorgang. Die Tabelle enthält drei Datensätze mit Artikelbestandsdaten, die mit den Feldern im Exponat übereinstimmen. Diese Datensätze wurden importiert, als der Index erstellt wurde. (Klicken Sie auf die Registerkarte Speicherdaten.) Wenn Benutzer ohne Filter suchen, werden alle drei Datensätze angezeigt.
Wenn Benutzer anhand der Beschreibung nach Elementen suchen, gibt der Such-Explorer keine Datensätze zurück. Der Search Explorer zeigt die Abfrage und die Ergebnisse für einen Test. Im Test versucht ein Benutzer, nach allen Elementen in der Tabelle zu suchen, deren Beschreibung das Wort bag enthält. (Klicken Sie auf die Registerkarte Such-Explorer.)
Sie müssen das Problem beheben.
Wählen Sie für jede der folgenden Anweisungen Ja aus, wenn die Anweisung wahr ist. Andernfalls wählen Sie Nein.
HINWEIS: Jede richtige Auswahl ist einen Punkt wert.
Answer:
Explanation:
Erläuterung
Kasten 1: Ja
Das Feld ItemDescription ist nicht durchsuchbar.
Kasten 2: Nein
Das Feld ItemDescription ist nicht durchsuchbar, aber wir müssten den Index neu erstellen.
Kasten 3: Ja
Ein Indexer in Azure Search ist ein Crawler, der durchsuchbare Daten und Metadaten aus einer externen Azure-Datenquelle extrahiert und einen Index basierend auf Feld-zu-Feld-Zuordnungen zwischen dem Index und Ihrer Datenquelle auffüllt.
Dieser Ansatz wird manchmal als "Pull-Modell" bezeichnet, da der Dienst Daten abruft, ohne dass Sie Code schreiben müssen, der Daten zu einem Index hinzufügt.
Kasten 4: Nein
Verweise:
https://docs.microsoft.com/en-us/azure/search/search-what-is-an-index
https://docs.microsoft.com/en-us/azure/search/search-indexer-overview
NEW QUESTION: 2
A company has a large on-premises Apache Hadoop cluster with a 20 PB HDFS database. The cluster is growing every quarter by roughly 200 instances and 1 PB. The company's goals are to enable resiliency for its Hadoop data, limit the impact of losing cluster nodes, and significantly reduce costs. The current cluster runs 24/7 and supports a variety of analysis workloads, including interactive queries and batch processing.
Which solution would meet these requirements with the LEAST expense and down time?
A. Use AWS Snowball to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workloads based on historical data from the on-premises cluster. Store the on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
B. Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster of similar size and configuration to the current cluster. Store the data on EMRFS. Minimize costs by using Reserved Instances. As the workload grows each quarter, purchase additional Reserved Instances and add to the cluster.
C. Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
D. Use AWS Direct Connect to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
Q: How should I choose between Snowmobile and Snowball?
To migrate large datasets of 10PB or more in a single location, you should use Snowmobile. For datasets less than 10PB or distributed in multiple locations, you should use Snowball. In addition, you should evaluate the amount of available bandwidth in your network backbone. If you have a high speed backbone with hundreds of Gb/s of spare throughput, then you can use Snowmobile to migrate the large datasets all at once. If you have limited bandwidth on your backbone, you should consider using multiple Snowballs to migrate the data incrementally.
Answer: C
NEW QUESTION: 3
セキュリティ管理者は、DDoS攻撃がDNSサーバーに影響を及ぼしていると疑っています。管理者は、ネットワーク上のworkstation01というホスト名でワークステーションにアクセスし、ipconfigコマンドから次の出力を取得します。
管理者は、ワークステーションからDNSサーバーに正常にpingを送信します。 DDoS攻撃が発生していないことを確認するには、ワークステーションから次のコマンドのどれを発行する必要がありますか？
A. 192.168.1.254を掘る
B. 192.168.1.26を掘る
C. www.google.comを掘る
D. workstation01.comを掘る
Answer: D
NEW QUESTION: 4
What is an example of a Configuration item (CI)?
A. Name of the supplier of an Underpinning contract (UC)
B. Service catalogue
C. Serial number
D. Location of a server
Answer: B