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NEW QUESTION: 1
Examine the parallelism parameters for you instance. Now examine the DSS_PLAN with parallel statement directives:


Which two are true about the DSS_PLAN resource manager plan?
A. ETL_GROUP sessions can collectively consume 64 parallel execution servers before queuing starts for this consumer group.
B. URGENT_GROUPS sessions will always be dequeued before sessions from other groups.
C. OTHER_GROUPS sessions are queued for maximum of six minutes.
D. URGENT_GROUP sessions will not be queued if 64 parallel execution servers are busy because their PARALLEL_TARGET_PERCENTAGE is not specified.
E. An ETL_GRP sessions will be switched to URGENT_GROUPS if the session requests more than eight parallel executions servers.
Answer: B,C
Explanation:
B:PARALLEL_QUEUE_TIMEOUT
Parallel Queue Timeout
When you use parallel statement queuing, if the database does not have sufficient
resources to execute a parallel statement, the statement is queued until the required
resources become available. However, there is a chance that a parallel statement may be
waiting in the parallel statement queue for longer than is desired. You can prevent such
scenarios by specifying the maximum time a parallel statement can wait in the parallel
statement queue.
The PARALLEL_QUEUE_TIMEOUT directive attribute enables you to specify the
maximum time, in seconds, that a parallel statement can wait in the parallel statement
queue before it is timed out. The PARALLEL_QUEUE_TIMEOUT attribute can be set for
each consumer group.
Incorrect:
Not C: ETL_GROUP PARALLEL_TARGET_PERCENTAGE is 50%. So ETL_GROUP can
only consume 32 servers.
Note:
*If you want more per-workload management, you must use the following directive
attributes:
/MGMT_Pn
Management attributes control how a parallel statement is selected from the parallel
statement queue for execution. You can prioritize the parallel statements of one consumer
group over another by setting a higher value for the management attributes of that group.
/PARALLEL_TARGET_PERCENTAGE
/PARALLEL_QUEUE_TIMEOUT
/PARALLEL_DEGREE_LIMIT_P1
* PARALLEL_DEGREE_LIMIT_P1 Degree of Parallelism Limit
You can limit the maximum degree of parallelism for any operation within a consumer group. The degree of parallelism is the number of parallel execution servers that are associated with a single operation. Use the PARALLEL_DEGREE_LIMIT_P1 directive attribute to specify the degree of parallelism for a consumer group.
The degree of parallelism limit applies to one operation within a consumer group; it does not limit the total degree of parallelism across all operations within the consumer group. However, you can combine both the PARALLEL_DEGREE_LIMIT_P1 and the PARALLEL_TARGET_PERCENTAGE directive attributes to achieve the desired control.
* PARALLEL_TARGET_PERCENTAGE Parallel Target Percentage
It is possible for a single consumer group to launch enough parallel statements to use all the available parallel servers. If this happens, when a high-priority parallel statement from a different consumer group is run, no parallel servers are available to allocate to this group. You can avoid such a scenario by limiting the number of parallel servers that can be used by a particular consumer group.
Use the PARALLEL_TARGET_PERCENTAGE directive attribute to specify the maximum percentage of the parallel server pool that a particular consumer group can use. The number of parallel servers used by a particular consumer group is counted as the sum of the parallel servers used by all sessions in that consumer group.

NEW QUESTION: 2
An LTM device is configure with the wildcard virtual servers displayed below.
A client connection is made to 172.24.31.14:443.
A. VS_HTTP_WILDCARD
B. VS_172_24_1_WILDCARD
C. VS_172_24_WILDCARD
D. VS_HTTPS_WILDCARD
Answer: C
Explanation:
Explanation
Match the network segment first and then port.

NEW QUESTION: 3
You are developing a customer web form that includes following HTML.
<input id="txtValue"/>
You need to develop the form so that customers can enter only a valid country code consisting of three English
alphabet characters.
Which code segment should you use?

A. Option C
B. Option A
C. Option B
D. Option D
Answer: A
Explanation:
* The val() method returns or sets the value attribute of the selected elements.
* HTML <input> pattern Attribute
The pattern attribute specifies a regular expression that the <input> element's value is checked against.
Example
An HTML form with an input field that can contain only three letters (no numbers or special characters):
<form action="demo_form.asp">
Country code: <input type="text" name="country_code"
pattern="[A-Za-z]{3}" title="Three letter country code">
<input type="submit">
</form>
Reference: HTML <input> pattern Attribute
http://www.w3schools.com/tags/att_input_pattern.asp

NEW QUESTION: 4
DRAG DROP
You are developing a SQL Server Analysis Services (SSAS) multidimensional project that is configured to source data from a Microsoft Azure SQL Database database. The cube is processed each night at midnight.
The largest partition in the cube takes 12 hours to process, and users are unable to access the cube until noon. The partition must be available for querying as soon as possible after processing commences.
You need to ensure that the partition is available for querying as soon as possible, without using source data to satisfy the query.
Which three actions should you perform in sequence? (To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.) Select and Place:

Answer:
Explanation:

Explanation/Reference:
Note:
* Processing mode has two possible options.
Regular. This is the default setting. When set to regular, partitions will be available to users after data has been loaded and aggregations are created completely.
Lazy Aggregations. When set to lazy aggregations, partitions will be available to user queries immediately after data has been loaded. Aggregations will be created as a separate background process while users start to query the partition.
* Lazy processing performs the task of building indexes and aggregations for dimensions and measure group partitions at a lower priority to reduce foreground processing time and to allow users to query the cube sooner. For lazy processing to occur, you must switch the ProcessingMode = LazyAggregations of your measure group partitions; by default this value is Regular (lazy processing is turned off). When processing a dimension with flexible aggregations such as parent-child or virtual dimension by using the processing enumeration of ProcessUpdate (such as to take into account of member name or hierarchy changes), lazy processing is initiated to ensure that the aggregations are rebuilt on the associated measure group partitions.
* Configure Lazy Processing for the cube, measure group, or partition. If you configure Lazy Processing, the dropped aggregations are recalculated as a background task. While the flexible aggregations are being recalculated, users can continue to query the cube (without the benefit of the flexible aggregations). While the flexible aggregations are being recalculated, queries that would benefit from the flexible aggregations run slower because Analysis Services resolves these queries by scanning the fact data and then summarizing the data at query time. As the flexible aggregations are recalculated, they become available incrementally on a partition-by-partition basis. For a given cube, Lazy Processing is not enabled by default.
You can configure it for a cube, measure group, or partition by changing the ProcessingMode property from Regular to LazyAggregations. To manage Lazy Processing, there are a series of server properties such as the LazyProcessing \ MaxObjectsInParallel setting, which controls the number of objects that can be lazy processed at a given time. By default it is set to 2. By increasing this number, you increase the number of objects processed in parallel; however, this also impacts query performance and should therefore be handled with care.
* Incorrect: With Bring Online Immediately enabled, during cache refresh all queries are directed to the relational source database to retrieve the latest data for end users. While this provides users with refreshed data, it can also result in reduced query performance given that Analysis Services needs to redirect queries to the relational source database.