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NEW QUESTION: 1





A. GROUP BY ROLLUP
B. PIVOT
C. MERGE
D. LEFT JOIN
E. CROSS JOIN
F. GROUP BY
G. UNPIVOT
H. GROUP BY CUBE
Answer: E
Explanation:
A cross join that does not have a WHERE clause produces the Cartesian product of the tables involved in the join. The size of a Cartesian product result set is the number of rows in the first table multiplied by the number of rows in the second table.
References: https://technet.microsoft.com/en-us/library/ms190690(v=sql.105).aspx

NEW QUESTION: 2
You plan to drop the column of a table that has Flashback Archiving enabled.
You want to ensure minimal service disruption.
How would you do this in your Oracle 12c Database?
A. Disable Flashback Archiving, set the column unused, and re-enable Flashback Archiving.
B. Drop the column using the ALTER TABLE statement.
C. Disassociate the Flashback Archive using the
DBMS_FLASHBACK_ARCHIVE.DISASSOCIATE_FBA procedure, drop the column, and re-associate the Flashback Archive using the DBMS_FLASHBACK_ARCHIVE.RESASSOCIATE_FBA procedure.
D. Disassociate the Flashback Archive using the
DBMS_FLASHBACK_ARCHIVE.DISASSOCIATE_FBA procedure, set the column unused, and re-associate the Flashback Archive using the DBMS_FLASHBACK_ARCHIVE.RESASSOCIATE_FBA procedure.
E. Disable Flashback Archiving, drop the column unused, and re-enable Flashback Archiving.
Answer: B

NEW QUESTION: 3
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio.
Which module you should use for each step? To answer, drag the appropriate modules to the correct steps. Each module may be used once or more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample