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NEW QUESTION: 1
Your network consists of an Active Directory Domain Services (AD DS) forest with 1,000 client computers that run Windows XP. 900 of the computers are on the local area network. 100 computers are portable computers that connect to the main office only once every few months.
You are planning to deploy Windows 7.
You need to generate a report of the software that is installed on all client computers. You need this information as soon as possible.
What should you use?
A. Microsoft System Center Essentials
B. Microsoft Desktop Optimization Pack
C. Microsoft System Center Data Protection Manager
D. Microsoft System Center Operations Manager
Answer: B
NEW QUESTION: 2
A. Profile properties of the Windows Azure application
B. Windows Azure session state
C. SQL Azure
D. Windows Azure application state
Answer: C
Explanation:
Explanation
SQL Database provides a relational database management system for Windows Azure and is based on SQL Server technology. With a SQL Database instance, you can easily provision and deploy relational database solutions to the cloud, and take advantage of a distributed data center that provides enterprise-class availability, scalability, and security with the benefits of built-in data protection and self-healing.
Incorrect:
Not A: Application State does not exist in Azure.
Not C: Profile properties stores personal, not global, information.
Not D: Session state is not global. Session states handles user information such as cookies, hidden fields, and query strings are some client-side options to tracking user state
NEW QUESTION: 3
Ann is testing the robustness of a marketing website through an intercepting proxy. She has intercepted the following HTTP request:
POST /login.aspx HTTP/1.1
Host: comptia.org
Content-type: text/html
txtUsername=ann&txtPassword=ann&alreadyLoggedIn=false&submit=true
Which of the following should Ann perform to test whether the website is susceptible to a simple authentication bypass?
A. Remove all of the post data and change the request to /login.aspx from POST to GET
B. Remove the txtUsername and txtPassword post data and toggle submit from true to false
C. Attempt to brute force all usernames and passwords using a password cracker
D. Remove the txtPassword post data and change alreadyLoggedIn from false to true
Answer: D
Explanation:
Explanation
The text "txtUsername=ann&txtPassword=ann" is an attempted login using a username of 'ann' and also a password of 'ann'.
The text "alreadyLoggedIn=false" is saying that Ann is not already logged in.
To test whether we can bypass the authentication, we can attempt the login without the password and we can see if we can bypass the 'alreadyloggedin' check by changing alreadyLoggedIn from false to true. If we are able to log in, then we have bypassed the authentication check.
NEW QUESTION: 4
You have a large number of web servers in an Auto Scalinggroup behind a load balancer. On an hourly basis,
you want to filter and process the logs to collect data on unique visitors, and then put that data in a durable
data store in order to run reports. Web servers in the Auto Scalinggroup are constantly launching and
terminating based on your scaling policies, but you do not want to lose any of the log data from these servers
during a stop/termination initiated by a user or by Auto Scaling. What two approaches will meet these
requirements? Choose two answers from the optionsgiven below.
A. Install an AWS Data Pipeline Logs Agent on every web server during the bootstrap process. Create a
log group object in AWS Data Pipeline, and define Metric Filters to move processed log data directly
from the web servers to Amazon Redshift and run reports every hour.
B. On the web servers, create a scheduled task that executes a script to rotate and transmit the logs to an
Amazon S3 bucket. Ensure that the operating system shutdown procedure triggers a logs transmission
when the Amazon EC2 instance is stopped/terminated. Use AWS Data Pipeline to move log data from
the Amazon S3 bucket to Amazon Redshift In order to process and run reports every hour.
C. Install an Amazon Cloudwatch Logs Agent on every web server during the bootstrap process. Create a
CloudWatch log group and define
Metric Filters to create custom metrics that track unique visitors from the streaming web server logs.
Create a scheduled task on an Amazon EC2 instance that runs every hour to generate a new report based
on the Cloudwatch custom metrics.