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NEW QUESTION: 1
솔루션 설계자는 개체 데이터를 저장하는 응용 프로그램을 작성하고 있습니다. 규정 준수 요구 사항에는 저장된 데이터가 변경되지 않는다고 명시되어 있습니다.
어떤 서비스가 이러한 요구 사항을 충족합니까?
A. 아마존 빙하
B. Amazon EFS
C. Amazon S3
D. AWS Storage Gateway
Answer: A
NEW QUESTION: 2
Ihr Netzwerk enthält eine Active Directory-Domäne mit dem Namen contoso.com. Die Domäne enthält zwei Mitgliedsserver mit den Namen Server1 und Server2.
Sie installieren die DHCP-Server-Serverrolle auf Server1 und Server2. Sie installieren die IPAM-Serverfunktion (IP Address Management) auf Server1.
Sie stellen fest, dass Sie Server1 oder Server2 in IPAM nicht erkennen können.
Sie müssen sicherstellen, dass Sie IPAM verwenden können, um die DHCP-Infrastruktur zu ermitteln.
Welche beiden Aktionen sollten Sie ausführen? (Jede richtige Antwort stellt einen Teil der Lösung dar. Wählen Sie zwei.)
A. Führen Sie auf Server1 das Cmdlet Add-IpamServerInventory aus.
B. Führen Sie auf Server1 und Server2 das Cmdlet Add-DhcpServerv4Policy aus.
C. Führen Sie auf Server2 das Cmdlet Add-DhcpServerInDc aus
D. Deinstallieren Sie auf Server1 die DHCP-Server-Serverrolle.
E. Erstellen Sie auf Server2 einen IPv4-Bereich.
Answer: A,C
Explanation:
Erläuterung
B. Das Cmdlet Add-IpamServerInventory fügt dem IPAM-Serverinventar (IP Address Management) einen neuen Infrastrukturserver hinzu. Verwenden Sie den vollqualifizierten Domänennamen (FQDN) des Servers, um das Serverinventar zu erweitern.
C. Das Cmdlet Add-DhcpServerInDC fügt den Computer, auf dem der DHCP-Serverdienst ausgeführt wird, zur Liste der autorisierten DHCP-Serverdienste (Dynamic Host Configuration Protocol) in Active Directory (AD) hinzu. Ein DHCP-Serverdienst, der auf einem Computer mit Domänenbeitritt ausgeführt wird, muss in AD autorisiert werden, damit IP-Adressen im Netzwerk geleast werden können.
Referenz: Add-IpamServerInventory; Add-DhcpServerInDC
NEW QUESTION: 3
Service Consumer A sends a message with a business document to Service A (1), which
writes the business document to Database A (2). Service A then forwards the business
document to Service B (3), which writes the business document to Database B (4).
Service B then responds to Service A with a message containing a failure or success code
(5) after which Service A responds to Service Consumer A with a message containing a
failure or success code (6). Upon receiving the message, Service Consumer A updates a
log table in Database B (7). The log entry is comprised of the entire business document.
Database A is dedicated to the Service A service architecture and Database B is a shared
database.
There are two problems with this service composition architecture that you are asked to
address: First, both Service Consumer A and Service B need to transform the business
document data from an XML format to a proprietary Comma Separated Value (CSV) in
order to write the data to Database B.
This has led to redundant data format transformation logic that has been difficult to keep in synch when Database B changes. Secondly, Service
A is an entity service that is being reused by several other service compositions. It has
lately developed reliability problems that have caused the service to become unavailable
for extended periods. What steps can be taken to solve these problems?
A. The Legacy Wrapper pattern can be applied so that data access to Database B is
separated into a new wrapper utility service. This way, the Data Format Transformation
pattern only needs to be applied within the logic of this new service which will expose a
standardized contract that both Service Consumer A and Service B can access. The
Reliable Messaging pattern can be applied so that acknowledgements are passed between
Service Consumer A and Service A and between Service A and Service B . The Service
Composability principle can be further applied to Service A in order to optimize its service
architecture for improved participation in multiple service compositions.
B. The Legacy Wrapper pattern can be applied so that data access to Database B is
separated into a new wrapper utility service. This way, the Data Format Transformation
pattern only needs to be applied within the logic of this new service which will expose a
standardized contract that both Service Consumer A and Service B can access. The
Asynchronous Queuing pattern can be applied so that messaging queues are established
between Service Consumer A and Service A and between Service A and Service B . The
Service Autonomy principle can be further applied to Service A in order to establish a more
isolated and reliable surrounding infrastructure.
C. None of the above.
D. The service composition can be redesigned with the application of the Contract
Centralization pattern so that instead of writing the business document to Database B,
Service Consumer A sends the business document to Service B instead. This way, Service
B would provide the only location where data format transformation logic for Database B
needs to be carried out, which further supports the application of the Service Reusability
principle. The Reliable Messaging pattern can be applied so that acknowledgements are
passed between Service Consumer A and Service A and between Service A and Service B
. The Service Composability principle can be further applied to Service A in order to
optimize its service architecture for improved participation in multiple service compositions.
Answer: B
NEW QUESTION: 4
機械学習タスクを適切なシナリオに一致させます。
答えるには、適切なタスクを左側の列から右側のシナリオにドラッグします。各タスクは、1回使用することも、複数回使用することも、まったく使用しないこともできます。
注：正しい選択はそれぞれ1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: Model evaluation
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.
Box 2: Feature engineering
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
Box 3: Feature selection
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml