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NEW QUESTION: 1
Refer to the exhibit.

Using a stateful packet firewall and given an inside ACL entry of permit ip 192.16.1.0 0.0.0.255 any, what would be the resulting dynamically configured ACL for the return traffic on the outside ACL?
A. permit tcp host 172.16.16.10 eq 80 host 192.168.1.11 eq 2300
B. permit ip host 172.16.16.10 eq 80 host 192.168.1.0 0.0.0.255 eq 2300
C. permit ip 172.16.16.10 eq 80 192.168.1.0 0.0.0.255 eq 2300
D. permit tcp any eq 80 host 192.168.1.11 eq 2300
Answer: A
Explanation:
http://www.cisco.com/en/US/docs/security/security_management/cisco_security_manager/security _manager/4.1/user/guide/fwinsp.html
Understanding Inspection Rules Inspection rules configure Context-Based Access Control (CBAC) inspection commands. CBAC inspects traffic that travels through the device to discover and manage state information for TCP and UDP sessions. The device uses this state information to create temporary openings to allow return traffic and additional data connections for permissible sessions. CBAC creates temporary openings in access lists at firewall interfaces. These openings are created when inspected traffic exits your internal network through the firewall. The openings allow returning traffic (that would normally be blocked) and additional data channels to enter your internal network back through the firewall. The traffic is allowed back through the firewall only if it is part of the same session as the original traffic that triggered inspection when exiting through the firewall.
Inspection rules are applied after your access rules, so any traffic that you deny in the access rule is not inspected. The traffic must be allowed by the access rules at both the input and output interfaces to be inspected. Whereas access rules allow you to control connections at layer 3 (network, IP) or 4 (transport, TCP or UDP protocol), you can use inspection rules to control traffic using application-layer protocol session information.
For all protocols, when you inspect the protocol, the device provides the following functions:
Automatically opens a return path for the traffic (reversing the source and destination addresses), so that you do not need to create an access rule to allow the return traffic. Each connection is considered a session, and the device maintains session state information and allows return traffic only for valid sessions. Protocols that use TCP contain explicit session information, whereas for UDP applications, the device models the equivalent of a session based on the source and destination addresses and the closeness in time of a sequence of UDP packets.
These temporary access lists are created dynamically and are removed at the end of a session.
Tracks sequence numbers in all TCP packets and drops those packets with sequence numbers that are not within expected ranges.
Uses timeout and threshold values to manage session state information, helping to determine when to drop sessions that do not become fully established. When a session is dropped, or reset, the device informs both the source and destination of the session to reset the connection, freeing up resources and helping to mitigate potential Denial of Service (DoS) attacks.

NEW QUESTION: 2

A. Data Collection
B. Statistics File
C. Fixed File
D. Var. File
Answer: D

NEW QUESTION: 3
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. the model name
B. the authentication key
C. the training endpoint
D. the REST endpoint
Answer: A,D
Explanation:
A: The trained model is stored as a Dataset module in the module palette. You can find it under My Datasets.
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
D: You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-batch-predictions-designer
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

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