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NEW QUESTION: 1
Which of the following security threats does NOT use software to extract sensitive information or credentials?
A. Man-in-the-Middle exploits
B. Malware
C. Grayware
D. Shoulder surfing
Answer: D

NEW QUESTION: 2
You plan to create a stored procedure that inserts data from an XML file to the OrderDetails table. The following is the signature of the stored procedure:

The following is the XSD file used to create the ValidateOrder schema collection:

You develop a code segment that retrieves the number of items and loops through each item. Each time the loop runs, a variable named @itemNumber is incremented.
You need to develop a code segment that retrieves the product ID of each item number in the loop.
Which code segment should you develop?
A. SET @productID = @items.value'/Root/Product['+ @itemNumber+ ']/productID', int)
B. SET @productID = @items.value'/Root/Product/productID', int)
C. SET @productID = @items.value'/Root/Product/@productID', int)
D. SET @productID = @items.value'/Root/Product['+ @itemNumber+ ']/@productID', int)
Answer: D
Explanation:
Topic 7, Fourth Coffee
Background
Corporate Information
Fourth Coffee is global restaurant chain. There are more than 5,000 locations worldwide.
Physical Locations
Currently a server at each location hosts a SQL Server 2012 instance. Each instance contains a database called StoreTransactions that stores all transactions from point of sale and uploads summary batches nightly.
Each server belongs to the COFFECORP domain. Local computer accounts access the StoreTransactions database at each store using sysadmin and datareaderwriter roles.
Planned changes
Fourth Coffee has three major initiatives:
* The IT department must consolidate the point of sales database infrastructure.
* The marketing department plans to launch a mobile application for micropayments.
* The finance department wants to deploy an internal tool that will help detect fraud.
Initially, the mobile application will allow customers to make micropayments to buy coffee and other items on the company web site. These micropayments may be sent as gifts to other users and redeemed within an hour of ownership transfer. Later versions will generate profiles based on customer activity that will push texts and ads generated by an analytics application.
When the consolidation is finished and the mobile application is in production, the micropayments and point of sale transactions will use the same database.
Existing Environment
Existing Application Environment
Some stores have been using several pilot versions of the micropayment application. Each version currently is in a database that is independent from the point of sales systems. Some versions have been used in field tests at local stores, and others are hosted at corporate servers. All pilot versions were developed by using SQL Server 2012.
Existing Support Infrastructure
The proposed database for consolidating micropayments and transactions is called CoffeeTransactions. The database is hosted on a SQL Server 2014 Enterprise Edition instance and has the following file structures:

Business Requirements
General Application Solution Requirements
The database infrastructure must support a phased global rollout of the micropayment application and consolidation.
The consolidated micropayment and point of sales database will be into a CoffeeTransactions database. The infrastructure also will include a new CoffeeAnalytics database for reporting on content from CoffeeTransactions.
Mobile applications will interact most frequently with the micropayment database for the following activities:
* Retrieving the current status of a micropayment;
* Modifying the status of the current micropayment; and
* Canceling the micropayment.
The mobile application will need to meet the following requirements:
* Communicate with web services that assign a new user to a micropayment by using a stored procedure named usp_AssignUser.
* Update the location of the user by using a stored procedure named usp_AddMobileLocation.
The fraud detection service will need to meet the following requirements:
* Query the current open micropayments for users who own multiple micropayments by using a stored procedure named usp.LookupConcurrentUsers.
* Persist the current user locations by using a stored procedure named usp_MobileLocationSnapshot.
* Look at the status of micropayments and mark micropayments for internal investigations.
* Move micropayments to dbo.POSException table by using a stored procedure named ups_DetectSuspiciousActivity.
* Detect micropayments that are flagged with a StatusId value that is greater than 3 and that occurred within the last minute.
The CoffeeAnalytics database will combine imports of the POSTransaction and MobileLocation tables to create a UserActivity table for reports on the trends in activity. Queries against the UserActivity table will include aggregated calculations on all columns that are not used in filters or groupings.
Micropayments need to be updated and queried for only a week after their creation by the mobile application or fraud detection services.
Performance
The most critical performance requirement is keeping the response time for any queries of the POSTransaction table predictable and fast.
Web service queries will take a higher priority in performance tuning decisions over the fraud detection agent queries.
Scalability
Queries of the user of a micropayment cannot return while the micropayment is being updated, but can show different users during different stages of the transaction.
The fraud detection service frequently will run queries over the micropayments that occur over different time periods that range between 30 seconds and ten minutes.
The POSTransaction table must have its structure optimized for hundreds of thousands of active micropayments that are updated frequently.
All changes to the POSTransaction table will require testing in order to confirm the expected throughput that will support the first year's performance requirements.
Updates of a user's location can tolerate some data loss.
Initial testing has determined that the POSTransaction and POSException tables will be migrated to an in-memory optimized table.
Availability
In order to minimize disruption at local stores during consolidation, nightly processes will restore the databases to a staging server at corporate headquarters.
Technical Requirements
Security
The sensitive nature of financial transactions in the store databases requires certification of the COFFECORP\Auditors group at corporate that will perform audits of the data. Members of the COFFECORP\Auditors group cannot have sysadmin or datawriter access to the database.
Compliance requires that the data stewards have access to any restored StoreTransactions database without changing any security settings at a database level.
Nightly batch processes are run by the services account in the COFFECORP\StoreAgent group and need to be able to restore and verify the schema of the store databases match.
No Windows group should have more access to store databases than is necessary.
Maintainability
You need to anticipate when POSTransaction table will need index maintenance.
When the daily maintenance finishes, micropayments that are one week old must be available for queries in UserActivity table but will be queried most frequently within their first week and will require support for in-memory queries for data within first week.
The maintenance of the UserActivity table must allow frequent maintenance on the day's most recent activities with minimal impact on the use of disk space and the resources available to queries. The processes that add data to the UserActivity table must be able to update data from any time period, even while maintenance is running.
The index maintenance strategy for the UserActivity table must provide the optimal structure for both maintainability and query performance.
All micropayments queries must include the most permissive isolation level available for the maximum throughput.
In the event of unexpected results, all stored procedures must provide error messages in text message to the calling web service.
Any modifications to stored procedures will require the minimal amount of schema changes necessary to increase the performance.
Performance
Stress testing of the mobile application on the proposed CoffeeTransactions database uncovered performance bottlenecks. The sys.dm_os_wait_stats Dynamic Management View (DMV) shows high wait_time values for WRTTELOG and PAGEIOLATCHJJP wait types when updating the MobileLocation table.
Updates to the MobileLocation table must have minimal impact on physical resources.
Supporting Infrastructure
The stored procedure usp_LookupConcurrentUsers has the current implementation:

The current stored procedure for persisting a user location is defined in the following code:

The current stored procedure for managing micropayments needing investigation is defined in the following code:

The current table, before implementing any performance enhancements, is defined as follows:

NEW QUESTION: 3
Which of the following techniques ensures that a value submitted in a form can only be yes or no ?
A. Use a hidden input field that has a value of yes or no .
B. Enable the safe_mode configuration directive.
C. Use a select list that only lets the user choose between yes and no .
D. None of the above.
Answer: D

NEW QUESTION: 4
An auction website enables users to bid on collectible items. The auction rules require that each bid is processed only once and in the order it was received. The current implementation is based on a fleet of Amazon EC2 web servers that write bid records into Amazon Kinesis Data Streams. A single t2.large instance has a cron job that runs the bid processor, which reads incoming bids from Kinesis Data Streams and processes each bid. The auction site is growing in popularity, but users are complaining that some bids are not registering.
Troubleshooting indicates that the bid processor is too slow during peak demand hours, sometimes crashes while processing, and occasionally loses track of which records is being processed.
What changes should make the bid processing more reliable?
A. Refactor the web application to post each incoming bid to an Amazon SNS topic in place of Kinesis Data Streams. Configure the SNS topic to trigger an AWS Lambda function that processes each bid as soon as a user submits it.
B. Refactor the web application to use the Amazon Kinesis Producer Library (KPL) when posting bids to Kinesis Data Streams. Refactor the bid processor to flag each record in Kinesis Data Streams as being unread, processing, and processed. At the start of each bid processing run, scan Kinesis Data Streams for unprocessed records.
C. Refactor the web application to post each incoming bid to an Amazon SQS FIFO queue in place of Kinesis Data Streams. Refactor the bid processor to continuously the SQS queue. Place the bid processing EC2 instance in an Auto Scaling group with a minimum and a maximum size of 1.
D. Switch the EC2 instance type from t2.large to a larger general compute instance type. Put the bid processor EC2 instances in an Auto Scaling group that scales out the number of EC2 instances running the bid processor, based on the IncomingRecords metric in Kinesis Data Streams.
Answer: C
Explanation:
Explanation
FIFO is better in this case compared to Kinesis, as it guarantee the order of the bid. Min Max 1, is okay as the SQS will hold the queue in case of failure of the instance, till it come back again.