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NEW QUESTION: 1
Ihr Netzwerk enthält eine Active Directory-Domäne mit dem Namen contoso.com.
Die Domäne enthält einen Computer mit dem Namen Computer1, auf dem Windows 10 ausgeführt wird.
Das Netzwerk verwendet den Adressraum 172.16.0.0/16.
Computer1 has an application named App1.exe that is located in D:\\Apps\\.
App1.exe ist so konfiguriert, dass Verbindungen über TCP-Port 8080 akzeptiert werden.
Sie müssen sicherstellen, dass App1.exe nur Verbindungen akzeptiert, wenn Computer1 mit dem Unternehmensnetzwerk verbunden ist.
Lösung: Sie konfigurieren eine eingehende Regel, die das TCP-Protokoll auf Port 8080 zulässt und für alle Profile gilt. Erfüllt dies das Ziel?
A. Nein
B. Ja
Answer: A
Explanation:
Explanation
You need to ensure that App1.exe can accept connections only when Computer1 is connected to the corporate network."Therefore, you should not create firewall rule for all three profiles.

NEW QUESTION: 2
A Solutions Architect is designing a public-facing web application for employees to upload images to their
social media account. The application consists of multiple Amazon
EC2 instances behind an elastic load balancer, an Amazon S3 bucket where uploaded images are stored, and
an Amazon DynamoDB table for storing image metadata.
Which AWS service can the Architect use to automate the process of updating metadata in the DynamoDB
table upon image upload?
A. AWS CloudFormation
B. Amazon SQS
C. Amazon CloudWatch
D. AWS Lambda
Answer: D

NEW QUESTION: 3
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?
A. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.
B. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
C. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
D. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
Answer: D
Explanation:
As time is less, use cloud NLP and entity is used to label general subjects, sentiment label for sentiment analysis.