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NEW QUESTION: 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You manage a project in Azure DevOps.
You need to prevent the configuration of the project from changing over time.
Solution: Implement Continuous Assurance for the project.
Does this meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Explanation
The basic idea behind Continuous Assurance (CA) is to setup the ability to check for "drift" from what is considered a secure snapshot of a system. Support for Continuous Assurance lets us treat security truly as a
'state' as opposed to a 'point in time' achievement. This is particularly important in today's context when
'continuous change' has become a norm.
There can be two types of drift:
* Drift involving 'baseline' configuration: This involves settings that have a fixed number of possible states (often pre-defined/statically determined ones). For instance, a SQL DB can have TDE encryption turned ON or OFF...or a Storage Account may have auditing turned ON however the log retention period may be less than 365 days.
* Drift involving 'stateful' configuration: There are settings which cannot be constrained within a finite set of well-known states. For instance, the IP addresses configured to have access to a SQL DB can be any (arbitrary) set of IP addresses. In such scenarios, usually human judgment is initially required to determine whether a particular configuration should be considered 'secure' or not. However, once that is done, it is important to ensure that there is no "stateful drift" from the attested configuration. (E.g., if, in a troubleshooting session, someone adds the IP address of a developer machine to the list, the Continuous Assurance feature should be able to identify the drift and generate notifications/alerts or even trigger 'auto-remediation' depending on the severity of the change).
Reference:
https://azsk.azurewebsites.net/04-Continous-Assurance/Readme.html

NEW QUESTION: 2
(Note: the student is being tested on concepts learnt during passive OS fingerprinting, basic TCP/IP connection concepts and the ability to read packet signatures from a sniff dump.). Snort has been used to capture packets on the network. On studying the packets, the penetration tester finds it to be abnormal. If you were the penetration tester, why would you find this abnormal?
What is odd about this attack? Choose the best answer.

A. These packets were crafted by a tool, they were not created by a standard IP stack.
B. The attacker wants to avoid creating a sub-carries connection that is not normally valid.
C. This is not a spoofed packet as the IP stack has increasing numbers for the three flags.
D. This is back orifice activity as the scan comes from port 31337.
Answer: D

NEW QUESTION: 3
展示を参照してください。これらの設定をそれぞれSwitchA、B、C、およびDに適用し、すべてのVLANトラフィックを通過させるトランクポートでスイッチが相互に接続されています。
ネットワーク上のトラフィックに関する説明は正しいですか？

A. 4つのスイッチすべてのホストは、VLAN 10でのみ正常に相互に通信できます。
B. レイヤー3デバイスがインストールされていない限り、どのスイッチのホストも同じスイッチの他のホストとのみ通信できます。
C. 4つのスイッチすべてのホストは、トランクポートを介して互いに正常に通信できます。
D. レイヤー3デバイスがインストールされていない限り、スイッチDのFastEthernet 0/1インターフェイス上のホストは、他のスイッチ上のホストと通信できません
Answer: D

NEW QUESTION: 4
Which of the following assumptions underlie the 'square root of time' rule used for computing VaR estimates over different time horizons?
I. the portfolio is static from day to day
II. asset returns are independent and identically distributed (i.i.d.)
III. volatility is constant over time
IV. no serial correlation in the forward projection of volatility
V. negative serial correlations exist in the time series of returns
VI. returns data display volatility clustering
A. I, II, V and VI
B. III, IV, V and VI
C. I, II, III and IV
D. I and II
Answer: C
Explanation:
Explanation
The square root of time rule can be used to convert, say a 1-day VaR to a 10-day VaR, by multiplying the known number by the square root of time to get the VaR over a different time horizon. However, there are key assumptions that underlie the application of this rule, and statements I to IV correctly state those assumptions.
Statements V and VI are not correct, because the application of the square root of time rule requires the absence of serial correlations, and also the absence of volatility clustering (ie independence). Therefore Choice
'c' is the correct answer.
The square root of time rule is also applied to convert volatility or standard deviation for one period to the volatility for a different time period. Remember that VaR is just a multiple of volatility, and therefore the assumptions that apply to the square root of time rule for VaR also apply to the same rule when used in the context of volatilities or standard deviation.