Questions and Answers: 166
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NEW QUESTION: 1
You have an Auto Scaling group associated with an Elastic Load Balancer (ELB). You have noticed that instances launched via the Auto Scaling group are being marked unhealthy due to an ELB health check, but these unhealthy instances are not being terminated.
What do you need to do to ensure trial instances marked unhealthy by the ELB will be terminated and replaced?
A. Increase the value for the Health check interval set on the Elastic Load Balancer
B. Add an Elastic Load Balancing health check to your Auto Scaling group
C. Change the health check set on the Elastic Load Balancer to use TCP rather than HTTP checks
D. Change the thresholds set on the Auto Scaling group health check
Answer: B
Explanation:
Explanation
http://docs.aws.amazon.com/AutoScaling/latest/DeveloperGuide/as-add-elb-healthcheck.html Add an Elastic Load Balancing Health Check to your Auto Scaling Group By default, an Auto Scaling group periodically reviews the results of EC2 instance status to determine the health state of each instance. However, if you have associated your Auto Scaling group with an Elastic Load Balancing load balancer, you can choose to use the Elastic Load Balancing health check. In this case, Auto Scaling determines the health status of your instances by checking the results of both the EC2 instance status check and the Elastic Load Balancing instance health check.
For information about EC2 instance status checks, see Monitor Instances With Status Checks in the Amazon EC2 User Guide for Linux Instances. For information about Elastic Load Balancing health checks, see Health Check in the Elastic Load Balancing Developer Guide.
This topic shows you how to add an Elastic Load Balancing health check to your Auto Scaling group, assuming that you have created a load balancer and have registered the load balancer with your Auto Scaling group. If you have not registered the load balancer with your Auto Scaling group, see Set Up a Scaled and Load-Balanced Application.
Auto Scaling marks an instance unhealthy if the calls to the Amazon EC2 action DescribeInstanceStatus return any state other than running, the system status shows impaired, or the calls to Elastic Load Balancing action DescribeInstanceHealth returns OutOfService in the instance state field.
If there are multiple load balancers associated with your Auto Scaling group, Auto Scaling checks the health state of your EC2 instances by making health check calls to each load balancer. For each call, if the Elastic Load Balancing action returns any state other than InService, the instance is marked as unhealthy. After Auto Scaling marks an instance as unhealthy, it remains in that state, even if subsequent calls from other load balancers return an InService state for the same instance.
NEW QUESTION: 2
Which of the following types of wireless attacks would be used specifically to impersonate another WAP in
order to gain unauthorized information from mobile users?
A. Evil twin
B. Rogue access point
C. IV attack
D. War driving
Answer: A
Explanation:
Section: Threats and Vulnerabilities
Explanation/Reference:
Explanation:
An evil twin, in the context of network security, is a rogue or fake wireless access point (WAP) that appears
as a genuine hotspot offered by a legitimate provider.
In an evil twin attack, an eavesdropper or hacker fraudulently creates this rogue hotspot to collect the
personal data of unsuspecting users. Sensitive data can be stolen by spying on a connection or using a
phishing technique.
For example, a hacker using an evil twin exploit may be positioned near an authentic Wi-Fi access point
and discover the service set identifier (SSID) and frequency. The hacker may then send a radio signal
using the exact same frequency and SSID. To end users, the rogue evil twin appears as their legitimate
hotspot with the same name.
In wireless transmissions, evil twins are not a new phenomenon. Historically, they were known as
honeypots or base station clones. With the advancement of wireless technology and the use of wireless
devices in public areas, it is very easy for novice users to set up evil twin exploits.
NEW QUESTION: 3
A data engineer needs to collect data from multiple Amazon Redshift clusters within a business and consolidate the data into a single central data warehouse. Data must be encrypted at all times while at rest or in flight.
What is the most scalable way to build this data collection process?
A. Connect to the source cluster over an SSL client connection, and write data records to Amazon Kinesis Firehose to load into your target data warehouse.
B. Use AWS KMS data key to run an UNLOAD ENCRYPTED command that stores the data in an unencrypted S3 bucket; run a COPY command to move the data into the target cluster.
C. Run an UNLOAD command that stores the data in an S3 bucket encrypted with an AWS KMS data key; run a COPY command to move the data into the target cluster.
D. Run an ETL process that connects to the source clusters using SSL to issue a SELECT query for new data, and then write to the target data warehouse using an INSERT command over another SSL secured connection.
Answer: B
Explanation:
The most scalable solutions are the UNLOAD/COPY solutions because they will work in parallel, which eliminates A and D as answers. Option C is incorrect because the data would not be encrypted in flight, and you cannot encrypt an entire bucket with a KMS key. Option B meets the encryption requirements, the UNLOAD ENCRYPTED command automatically stores the data encrypted using-client side encryption and uses HTTPS to encrypt the data during the transfer to S3.
NEW QUESTION: 4
You hired a female employee on January 1, 2011. This employee got married on June 12, 2011. You received a request from the employee on July 11, 2012 to change her last name from the date of her marriage. You changed the last name of the employee as requested on the same day.
What is the effective start date of personal details displayed by the system as of August 15, 2011?
A. January 1, 2011
B. July 11, 2011
C. August 15, 2011
D. June 12, 2011
Answer: D
Explanation:
To maintain employee data effectively Oracle HCM is using a mechanism called date tracking.The main motive behind the date track mode is to maintain past,present and future data effectively.The various update date track modes are: CORRECTION : Over writes the data. No history will maintain.UPDATE : Keeps the history and new change will effect as of effective dateUPDATE_CHANGE_INSERT : Inserts the record and preserves the futureUPDATE_OVERRIDE : Inserts the record and overrides the future Example: