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NEW QUESTION: 1
Refer to the exhibit.
Which OSPFv3 routes will be visible in the routing table of R2?
A. 2001:12::1/128
B. No OSPFv3 routes will be visible.
C. 2001:12::1/128, 2001:112::1/128
D. 2001:12::2/128
Answer: B
Explanation:
Explanation/Reference:
Explanation:
The command "ipv6 unicast-routing" needs to be configured on both routers before any IPv6 routes will be seen.

NEW QUESTION: 2

A. Option C
B. Option D
C. Option B
D. Option A
Answer: C
Explanation:
The awrddrpt.sql report is the Automated Workload Repository Compare Period
Report. The awrddrpt.sql script is located in the $ORACLE_HOME/rdbms/admin directory.
Incorrect:
Not A: Compare Period ADDM
Use this report to perform a high-level comparison of one workload replay to its capture or to
another replay of the same capture. Only workload replays that contain at least 5 minutes of
database time can be compared using this report.

NEW QUESTION: 3
You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.
You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:
* The learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.
* Batch size must be 16, 32 and 64.
* Keep probability must be a value selected from a uniform distribution between the range of 0.05 and 0.1.
You need to use the param_sampling method of the Python API for the Azure Machine Learning Service.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Example:
from azureml.train.hyperdrive import RandomParameterSampling
param_sampling = RandomParameterSampling( {
"learning_rate": normal(10, 3),
"keep_probability": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64)
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters