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NEW QUESTION: 1
You administer a Microsoft SQL Server 2014 database named Contoso on a server named Server01.
You need to write messages to the Application Log when users are added to or removed from a fixed server role in Server01.
What should you create?
A. A SQL Profiler Trace
B. A Database Audit Specification
C. A Policy
D. A Resource Pool
E. A Server Audit Specification
F. An Extended Event session
G. An Alert
Answer: E
Explanation:
The SQL Server Audit feature enables you to audit server-level and database-level groups of events and individual events.
Audits can have the following categories of actions:
Server-level. These actions include server operations, such as management changes, such as in this question, and logon and logoff operations.
Database-level. These actions encompass data manipulation languages (DML) and data definition language (DDL) operations.
Audit-level. These actions include actions in the auditing process.
References: http://technet.microsoft.com/en-us/library/cc280663(v=sql.105).aspx

NEW QUESTION: 2
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is
99.1%, but the Data Scientist has been asked to reduce the number of false negatives.
Predicted 0 1
Actual 0 99,966| 34 1 877|123
Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)
A. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
B. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
C. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
D. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.
E. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
Answer: E

NEW QUESTION: 3
Even a user with "View all data" (VAD) permission cannot view hidden folders as well as folders shared to no one.
A. False
B. True
Answer: A

NEW QUESTION: 4
You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and Sample module to the experiment.
You need to configure the module. You must meet the following requirements:
Divide the data into subsets
Assign the rows into folds using a round-robin method
Allow rows in the dataset to be reused
How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.


Answer:
Explanation:

Explanation:
Use the Split data into partitions option when you want to divide the dataset into subsets of the dat a. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.
Add the Partition and Sample module to your experiment in Studio (classic), and connect the dataset.
For Partition or sample mode, select Assign to Folds.
Use replacement in the partitioning: Select this option if you want the sampled row to be put back into the pool of rows for potential reuse. As a result, the same row might be assigned to several folds.
If you do not use replacement (the default option), the sampled row is not put back into the pool of rows for potential reuse. As a result, each row can be assigned to only one fold.
Randomized split: Select this option if you want rows to be randomly assigned to folds.
If you do not select this option, rows are assigned to folds using the round-robin method.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

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