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NEW QUESTION: 1
What is the WOS?
A. a redundant copy of commonly read data, cached in memory
B. a column-store, disk-based method for storing data in Vertica
C. a row-store, memory-based method for adding data to Vertica
D. a redundant write-only location used to improve node uptime
Answer: B
NEW QUESTION: 2
DRAG DROP
Answer:
Explanation:
NEW QUESTION: 3
You create a binary classification model by using Azure Machine Learning Studio.
You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:
* iterate all possible combinations of hyperparameters
* minimize computing resources required to perform the sweep
* You need to perform a parameter sweep of the model.
Which parameter sweep mode should you use?
A. Random grid
B. Entire grid
C. Random sweep
D. Sweep clustering
E. Random seed
Answer: A
Explanation:
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.
If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.
Incorrect Answers:
B: If you are building a clustering model, use Sweep Clustering to automatically determine the optimum number of clusters and other parameters.
C: Entire grid: When you select this option, the module loops over a grid predefined by the system, to try different combinations and identify the best learner. This option is useful for cases where you don't know what the best parameter settings might be and want to try all possible combination of values.
E: If you choose a random sweep, you can specify how many times the model should be trained, using a random combination of parameter values.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model-hyperparameters
NEW QUESTION: 4
Refer to the exhibit.
ROUTE.com is planning to implement load balancing for traffic between hosts on the 172.16.10.0/24 and 172.16.20./24 networks. You have been asked to review the implementation plan for this project. Which statement about the plan is true?
A. It is complete as written.
B. It should include a task to configure EIGRP multipath equal to 2 on R1 and R4.
C. It should include a task that establishes a baseline before and after the configuration has been changed.
D. It should include a task to implement OSPF because it handles unequal cost load balancing most efficiently using variance.
Answer: A
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