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NEW QUESTION: 1
A salaried individual, aged 45 years, was awarded a car of market value Rs. 6,50,000 by his credit card company in a draw on 20th December 2012. There was no TDS by the company. He has total income from salary of Rs. 8,45,000 in the previous year 2012-13. He saved a total of Rs. 1,80,000 under different investment instruments eligible for exemption u/s 80C and Rs. 25,000 was paid by him on 5th January, 2013 towards his health insurance policy. Find his tax liability for AY2013-14.
A. Rs. 2,77,070/
B. Rs. 78,280/
C. Rs. 2,79,130/
D. Rs. 2,60,590/
Answer: C
NEW QUESTION: 2
You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.
You must use Hyperdrive to try combinations of the following hyperparameter values. You must not apply an early termination policy.
learning_rate: any value between 0.001 and 0.1
* batch_size: 16, 32, or 64
You need to configure the sampling method for the Hyperdrive experiment Which two sampling methods can you use? Each correct answer is a complete solution.
NOTE: Each correct selection is worth one point.
A. Bayesian sampling
B. Grid sampling
C. No sampling
D. Random sampling
Answer: A,D
Explanation:
Explanation
C: Bayesian sampling is based on the Bayesian optimization algorithm and makes intelligent choices on the hyperparameter values to sample next. It picks the sample based on how the previous samples performed, such that the new sample improves the reported primary metric.
Bayesian sampling does not support any early termination policy
Example:
from azureml.train.hyperdrive import BayesianParameterSampling
from azureml.train.hyperdrive import uniform, choice
param_sampling = BayesianParameterSampling( {
"learning_rate": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64, 128)
}
)
D: 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.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION: 3
A brick-and-mortar company purchases AEM.
Which application component is NOT well suited to be implemented in AEM?
A. Store finder as AEM does NOT support geospatial search.
B. Store finder as AEM does NOT integrate with Google Maps.
C. Training video site section as AEM does NOT bundle a video player.
D. Training video site section as AEM is unable to record user interactions.
Answer: D