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NEW QUESTION: 1
Identify two benefits provided by using sign-on (SSO) in a High availability (HA) application .(Choose Two.)
A. To allow for cookie persistence on the browser
B. To automatically authenticate users on a new server following a server crash
C. To redirect the user to a new server quince (pause)
D. To allow the movement of stateful data between application server
Answer: A,B
NEW QUESTION: 2
A developer uses a before insert trigger on the Lead object to fetch the Territory__c object, where the Territory__c.PostalCode__c matches the Lead.PostalCode. The code fails when the developer uses the Apex Data Loader to insert 10,000 Lead records. The developer has the following code block: Line-01: for (Lead l : Trigger.new){Line-02: if (l.PostalCode != null) {Line-03: List<Territory__c> terrList = [SELECT Id FROM Territory__c WHERE PostalCode__c = :l.PostalCode];Line-04: if(terrList.size() > 0) Line-05:
l.Territory__c = terrList[0].Id; Line-06: }Line-07: }Which line of code is causing the code block to fail?
A. Line-03: A SOQL query is located inside of the for loop code.
B. Line-01: Trigger:new is not valid in a before insert Trigger.
C. Line-05: The Lead in a before insert trigger cannot be updated.
D. Line-02: A NullPointer exception is thrown if PostalCode is null.
Answer: A
NEW QUESTION: 3
You are running a training experiment on remote compute in Azure Machine Learning.
The experiment is configured to use a conda environment that includes the mlflow and azureml-contrib-run packages.
You must use MLflow as the logging package for tracking metrics generated in the experiment.
You need to complete the script for the experiment.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: import mlflow
Import the mlflow and Workspace classes to access MLflow's tracking URI and configure your workspace.
Box 2: mlflow.start_run()
Set the MLflow experiment name with set_experiment() and start your training run with start_run().
Box 3: mlflow.log_metric(' ..')
Use log_metric() to activate the MLflow logging API and begin logging your training run metrics.
Box 4: mlflow.end_run()
Close the run:
run.endRun()
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow