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NEW QUESTION: 1
An administrator pushes a new configuration from Panorama to a pair of firewalls that are configured as an active/passive HA pair. Which NGFW receives the configuration from Panorama?
A. Both the active and passive firewalls independently, with no synchronization afterward
B. The Passive firewall, which then synchronizes to the active firewall
C. Both the active and passive firewalls, which then synchronize with each other
D. The active firewall, which then synchronizes to the passive firewall
Answer: A
Explanation:
Palo Alto NetworksPanorama 7.0 Administrator's Guide *77Manage FirewallsManage Device GroupsManage Device GroupspAdd a Device GrouppCreate a Device Group HierarchypCreate Objects for Use in Shared or Device Group PolicypRevert to Inherited Object ValuespManage Unused Shared ObjectspManage Precedence of Inherited ObjectspMove or Clone a Policy Rule or Object to a Different Device GrouppSelect a URL Filtering Vendor on PanoramapPush a Policy Rule to a Subset of FirewallspManage the Rule HierarchyAdd a Device GroupAfter adding firewalls (see Add a Firewall as a Managed Device), you can group them into Device Groups (up to 256), as follows. Be sure to assign both firewalls in an active-passive high availability (HA) configuration to the same device group so that Panorama will push the same policy rules and objects to those firewalls. #############PAN-OS doesn't synchronize pushed rules across HA peers.######### To manage rules and objects at different administrative levels in your organization, Create a Device Group Hierarchy.
https://docs.paloaltonetworks.com/panorama/8-0/panorama-admin/manage-firewalls/transition-a-firewall-to-panorama-management/migrate-a-firewall-ha-pair-to-panorama-management
NEW QUESTION: 2
A. Option A
B. Option E
C. Option C
D. Option B
E. Option D
Answer: B,E
NEW QUESTION: 3
You are solving a classification task.
The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use?
A. Filter Based Feature Selection
B. Synthetic Minority Oversampling Technique (SMOTE)
C. Fisher Linear Discriminant Analysis.
D. Permutation Feature Importance
Answer: B
Explanation:
Use the SMOTE module in Azure Machine Learning Studio (classic) to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
NEW QUESTION: 4
View the exhibit and examine the structure of the EMPLOYEES table.
You want to select all employees having 100 as their MANAGER_ID manages and their manager.
You want the output in two columns: the first column should have the employee's manager's LAST_NAME and the second column should have the employee's LAST_NAME.
Which SQL statement would you execute?
A. SELECT m.last_name "Manager", e.last_name "Employee"FROM employees m JOIN employees eWHERE m.employee_id = e.manager_id AND e.manager_id=100
B. SELECT m.last_name "Manager", e.last_name "Employee"FROM employees m JOIN employees eON e.employee_id = m.manager_idWHERE m.manager_id=100;
C. SELECT m.last_name "Manager", e.last_name "Employee"FROM employees m JOIN employees eON m.employee_id = e.manager_idWHERE m.manager_id=100;
D. SELECT m.last_name "Manager", e.last_name "Employee"FROM employees m JOIN employees eON m.employee_id = e.manager_idWHERE e.manager_id=100;
Answer: D