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Every question raised by you would receive a careful reply.

NEW QUESTION: 1
You add "sling.run.modes=publish/f in sling.properties and start the instance using the command "java -jar aem-6-p4502.jar -r author". In which mode will the instance start?
A. Both author and publish.
B. Due to conflict in the run modes, AEM will not start.
C. Author
D. Publish
Answer: D

NEW QUESTION: 2
A company is implementing a publish-subscribe (Pub/Sub) messaging component by using Azure Service Bus. You are developing the first subscription application.
In the Azure portal you see that messages are being sent to the subscription for each topic. You create and initialize a subscription client object by supplying the correct details, but the subscription application is still not consuming the messages.
You need to complete the source code of the subscription client
What should you do?
A. await subscriptionClient.CloseAsync();
B. await subscriptionClient.AddRuleAsync(new RuleDescription(RuleDescription.DefaultRuleName, new TrueFilter()));
C. subscriptionClient.RegisterMessageHandler(ProcessMessagesAsync, messageHandlerOptions);
D. subscriptionClient = new SubscriptionClient(ServiceBusConnectionString, Case Study:Name, SubscriptionName);
Answer: C
Explanation:
Using topic client, call RegisterMessageHandler which is used to receive messages continuously from the entity. It registers a message handler and begins a new thread to receive messages. This handler is waited on every time a new message is received by the receiver.
subscriptionClient.RegisterMessageHandler(ReceiveMessagesAsync, messageHandlerOptions); References:
https://www.c-sharpcorner.com/article/azure-service-bus-topic-and-subscription-pub-sub/

NEW QUESTION: 3
You create a binary classification model to predict whether a person has a disease.
You need to detect possible classification errors.
Which error type should you choose for each description? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Answer:
Explanation:

Explanation:
Box 1: True Positive
A true positive is an outcome where the model correctly predicts the positive class Box 2: True Negative A true negative is an outcome where the model correctly predicts the negative class.
Box 3: False Positive
A false positive is an outcome where the model incorrectly predicts the positive class.
Box 4: False Negative
A false negative is an outcome where the model incorrectly predicts the negative class.
Note: Let's make the following definitions:
"Wolf" is a positive class.
"No wolf" is a negative class.
We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:
Reference:
https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative