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NEW QUESTION: 1
Refer to the exhibit.
Which traffic is permitted on the inside interface without any interface ACLs configured?
A. any IP traffic input to the inside interface destined to any lower security level interfaces
B. any IP traffic input to the inside interface
C. No input traffic is permitted on the inside interface
D. only HTTP traffic output from the inside interface
E. only HTTP traffic input to the inside interface
F. No output traffic is permitted on the inside interface
Answer: E
NEW QUESTION: 2
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services.
The experiment for fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
End of repeated scenario.
The users will use different data sources that follow a standard format. The users will receive results in a standard format by using the fraud prediction web service. The results will be saved to a location specified by the users.
You need to provide the users with the ability to get results for different risk tolerances without affecting the calculation of the model.
Which three modules should be configured to use the Web Service Parameters? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Select Columns in Dataset
B. Time Series Anomaly Detection
C. Export Data
D. Import Data
E. Evaluate Model
Answer: C,D,E
Explanation:
Explanation/Reference:
Explanation:
A: From scenario: The experiment for fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
BD: An Azure Machine Learning web service is created by publishing an experiment that contains modules with configurable parameters. In some cases, you may want to change the module behavior while the web service is running. Web Service Parameters allow you to do this task.
A common example is setting up the Import Data module so that the user of the published web service can specify a different data source when the web service is accessed. Or configuring the Export Data module so that a different destination can be specified References: https://docs.microsoft.com/en-us/azure/machine-learning/studio/web-service-parameters
NEW QUESTION: 3
If your application performs operations or workflows that take a long time to complete, what service can the Elastic Beanstalk environment do for you?
A. Manages the ELB and running a daemon process on each instance
B. Manages Lambda functions and running a daemon process on each instance
C. Manages a Amazon SQS queue and running a daemon process on each instance
D. Manages a Amazon SNS Topic and running a daemon process on each instance
Answer: C
Explanation:
Explanation
Elastic Beanstalk simplifies this process by managing the Amazon SQS queue and running a daemon process on each instance that reads from the queue for you.
When
the daemon pulls an item from the queue, it sends an HTTP POST request locally to http://localhost/ with the contents of the queue message in the body. All that your application needs to do is perform the long-running task in response to the POST.
For more information Elastic Beanstalk managing worker environments, please visit the below URL:
http://docs.aws.amazon.com/elasticbeanstalk/latest/dg/using-features-managing-env-tiers.htmI
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