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NEW QUESTION: 1
You create a Cloud Volumes Service for AWS NFS volume for your database. You want to limit the volume to only be accessible In the VPC where your database servers reside.
How would you limit this access?
A. Apply a security group to the Cloud Volume Service for AWS,
B. Apply a security group to the database servers.
C. Set the export policy rule For the volume.
D. Set share permissions so that only the host and users can access.
Answer: B
NEW QUESTION: 2
If demand or lead time is greater than forecast, then what will occur?
A. Unpredictable stock
B. Vulnerable stock
C. Stockout
D. Invaluable stock
Answer: C
NEW QUESTION: 3
A. Option B
B. Option E
C. Option F
D. Option A
E. Option C
F. Option D
Answer: A,C,D
NEW QUESTION: 4
You need to implement a model development strategy to determine a user's tendency to respond to an ad.
Which technique should you use?
A. Use a Split Rows module to partition the data based on centroid distance.
B. Use a Split Rows module to partition the data based on distance travelled to the event.
C. Use a Relative Expression Split module to partition the data based on distance travelled to the event.
D. Use a Relative Expression Split module to partition the data based on centroid distance.
Answer: D
Explanation:
Explanation
Split Data partitions the rows of a dataset into two distinct sets.
The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.
Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.
Scenario:
Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
The distribution of features across training and production data are not consistent References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data