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NEW QUESTION: 1
What does static application security testing (SAST) offer as a tool to the testers?
A. Production system scanning
B. Injection attempts
C. Live testing
D. Source code access
Answer: D
Explanation:
Static application security testing (SAST) is conducted with knowledge of the system, including source code, and is done against offline systems.

NEW QUESTION: 2
You are creating a SQL Server 2008 Reporting Services (SSRS) report which would show from a SQL Server 2005 Analysis Services database for Company.com.
You should make sure that the report shows member attributes and extended attributes. Which is the correct answer?
A. You should utilize the DBCC SHRINKFILE.
B. You should utilize the Web Service.
C. You should utilize the OLE DB for SQL Server data source.
D. You should utilize the SQL Server Analysis Services data source.
Answer: D

NEW QUESTION: 3
You are building a classification experiment in Azure Machine Learning.
You need to ensure that you can use the Evaluate Model module in the experiment.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Connect the output of the Score Model modules to the input of the Train Model modules and the input of the Split Data modules.
B. Connect the input of the Score Model modules to the output of the Evaluate Model module.
C. Connect the input of the Score Model modules to the output of the Train Model modules and the output Split Data modules.
D. Connect the output of the Score Model modules to the input of the Evaluate Model module.
Answer: C,D
Explanation:
Explanation/Reference:
Explanation:
Use the training data.
To evaluate a model, you must connect a dataset that contains a set of input columns and scores. If no other data is available, you can use your original dataset.
1. Connect the Scored dataset output of the Score Model to the input of Evaluate Model.
2. Click Evaluate Model module, and select Run selected to generate the evaluation scores.
Use testing data
A common scenario in machine learning is to separate your original data set into training and testing datasets, using the Split module, or the Partition and Sample module.
1. Connect the Scored dataset output of the Score Model to the input of Evaluate Model.
2. Connect the output of the Split Data module that contains the testing data to the right-hand input of Evaluate Model.
3. Click Evaluate Model module, and select Run selected to generate the evaluation scores.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate- model

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