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NEW QUESTION: 1
レポートには、顧客、請求サイクル、クレジットカード、および有効期限の列が含まれます。マネージャーは、次の条件を満たす顧客のみをレポートに表示するよう要求します。
*顧客は毎月の請求サイクルにあります
*クレジットカードはMoreCashまたはVista
*クレジットカードの有効期限は
どのフィルタリング条件のセットが目的のデータを含むレポートを返しますか？
A. クレジットカードがMoreCashまたはクレジットカードに等しい[Vistaに等しく、請求サイクルが毎月に等しく、有効期限が次の60日に等しい
B. クレジットカードがMoreCashに等しい、またはクレジットカードがVistaに等しく、請求サイクルが四半期ごとではなく、有効期限が60日未満である
C. クレジットカードはMoreCashまたはVistaに等しく、請求サイクルは毎月、有効期限は次の60日に等しい
D. クレジットカードがMoreCashに等しく、クレジットカードがVistaに等しく、請求サイクルが毎月に等しく、有効期限が今日より60日長い
Answer: B
NEW QUESTION: 2
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Bitte beachten Sie, dass Sie nach dem Einreichen Ihrer Arbeit durch Klicken auf die Schaltfläche Weiter in einem Labor NICHT mehr zum Labor zurückkehren können.
Sie können jetzt auf Weiter klicken, um zum Labor zu gelangen.
Laborinformationen
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Answer:
Explanation:
You need to configure the App Registrations setting in Azure Active Directory.
1. Go to the Azure Active Directory admin center.
2. Select Azure Active Directory.
3. Select 'User settings'
4. In the 'App registrations' section, toggle the 'Users can register applications' setting to No.
5. Click Save to save the changes.
NEW QUESTION: 3
You deploy a model in Azure Container Instance.
You must use the Azure Machine Learning SDK to call the model API.
You need to invoke the deployed model using native SDK classes and methods.
How should you complete the command? To answer, select the appropriate options in the answer areas.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: from azureml.core.webservice import Webservice
The following code shows how to use the SDK to update the model, environment, and entry script for a web service to Azure Container Instances:
from azureml.core import Environment
from azureml.core.webservice import Webservice
from azureml.core.model import Model, InferenceConfig
Box 2: predictions = service.run(input_json)
Example: The following code demonstrates sending data to the service:
import json
test_sample = json.dumps({'data': [
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
]})
test_sample = bytes(test_sample, encoding='utf8')
prediction = service.run(input_data=test_sample)
print(prediction)
Reference:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/how-to-deploy-azure-container-instance
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment
NEW QUESTION: 4
The Moore Corporation is considering the acquisition of a new machine. The machine can be purchased
for $90,000; it will cost $6,000 to transport to Moore's plant and $9,000 to install. It is estimated that the
machine will last 10 years, and it is expected to have an estimated salvage value of $5,000. Over its
1 0-year life, the machine is expected to produce 2,000 units per year with a selling price of $500 and
combined material and labor costs of $450 per unit. Federal tax regulations permit machines of this type
to be depreciated using the straight-line method over 5 years with no estimated salvage value. Moore has
a marginal tax rate of 40 percent.
What is the net cash flow for the tenth year of the project that Moore Corporation should use in a capital
budgeting analysis?
A. $81,000
B. $68,400
C. $60,000
D. $63,000
Answer: D
Explanation:
Choice "c" is correct. $63,000 net cash flow for the tenth year.
Alternate Computation:
In year 10, Moore will generate a $100,000 profit from the incremental sales (2000 units * ($500 -$450)).
This profit will be taxed at 40%, so the net after-tax increase in cash flow is $60,000. The machine is fully
depreciated in year 10 because it was depreciated over a 5-year life. The tax basis of the machine is zero
on the date Moore receives a $5,000 salvage value for the machine. The gain on the machine of $5,000
($5,000 SV $0 Basis) is taxed at 40%, or $2,000 in total tax outflow for the gain, so the net inflows on the
salvage is $3,000. Therefore, the total after-tax cash flows in year 10 for the new machine is $60,000 +
$ 3,000 = $63,000.