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NEW QUESTION: 1
A Windows Communication Foundation (WCF) solution uses the following contracts. (Line numbers are included for reference only.)
01 [ServiceContract(CallbackContract=typeof(INameService))]
02 public interface IGreetingService
03 {
04 [OperationContract]
05 string GetMessage();
06 }
07
08 [ServiceContract]
09 public interface INameService
10 {
11 [OperationContract]
12 string GetName();
13 }
When the client calls GetMessage on the service interface, the service calls GetName on the client
callback.
In the client, the class NameService implements the callback contract. The client channel is created as
follows:
22 InstanceContext callbackContext = new InstanceContext(new NameService
("client"));
23 ...
24 ...
25 DuplexChannelFactory<IGreetingService> factory = new
DuplexChannelFactory<IGreetingService>(typeof(NameService), binding, address);
26 IGreetingService greetingService = factory.CreateChannel();
You need to ensure that the service callback is processed by the instance of NameService.
What are two possible ways to achieve this goal? (Each correct answer presents a complete solution.
Choose two.)
A. Add the following code segment after line 26:
callbackContext.IncomingChannels.Add((IDuplexChannel)greetingService);
B. Change line 26 to the following code segment:
IGreetingService greetingServicefactory = CreateChannel(callbackContext);
C. Change line 25 to the following code segment:
DuplexChannelFactory<IGreetingService> factory = new DuplexChannelFactory<IGreetingService>(callbackContext, binding, address);
D. Add the following code segment after line 26:
callbackContext.OutgoingChannels.Add((IDuplexChannel)greetingService);
Answer: B,C
Explanation:
Explanation/Reference:
Example 1:
// Construct InstanceContext to handle messages on callback interface InstanceContext instanceContext = new InstanceContext(new CallbackHandler());
// Create a client CalculatorDuplexClient client = new CalculatorDuplexClient(instanceContext);

NEW QUESTION: 2
A company has an Amazon RDS-managed online transaction processing system that has very heavy read and write. The Solutions Architect notices throughput issues with the system.
How can the responsiveness of the primary database be improved?
A. Use asynchronous replication for standby to maximize throughput during peak demand.
B. Offload SELECT query that needs the most current data to READ replica.
C. Offload SELECT queries that can tolerate stale data to READ replica.
D. Offload SELECT and UPDATE queries to READ replica.
Answer: A

NEW QUESTION: 3
The company's sales team plans to work late to provide the Chief Executive Officer (CEO) with a special report of sales before the quarter ends. After working for several hours, the team finds they cannot save or print the reports.
Which of the following controls is preventing them from completing their work?
A. Mandatory access control
B. Role-based access control
C. Time of Day access control
D. Discretionary access control
Answer: C
Explanation:
Time of day restrictions limit when users can access specific systems based on the time of day or week. It can limit access to sensitive environments to normal business hours when oversight and monitoring can be performed to prevent fraud, abuse, or intrusion. In this case, the sales team is prevented from saving or printing reports after a certain time.

NEW QUESTION: 4
You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Services (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.
You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation

Step 1: Create and start a Compute Instance
To train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.
There are four kinds of compute resource you can create:
Compute Instances: Development workstations that data scientists can use to work with data and models.
Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code.
Inference Clusters: Deployment targets for predictive services that use your trained models.
Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.
Step 2: Create and run a training pipeline..
After you've used data transformations to prepare the data, you can use it to train a machine learning model.
Create and run a training pipeline
Step 3: Create and run a real-time inference pipeline
After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This pipeline will form the basis for a predictive service that you can publish for applications to use.
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/

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