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NEW QUESTION: 1
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.)
Answer:
Explanation:
Explanation:
Box 1:
Box 2:
Box 3:
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.
NEW QUESTION: 2
What is high availability?
A. redundant infrastructure
B. clustering of computer systems
C. continuous operation of computing systems
D. reduced MTBF
Answer: C
Explanation:
Section: (none)
Explanation/Reference:
NEW QUESTION: 3
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario.
Your network contains an Active Directory forest named contoso.com
You deploy an Azure Stack integrated system named Prod to a production environment. You also deploy an Azure Stack integrated system named Dev to a development environment. The developers who access Dev change frequently.
The Azure Stack integrated systems and the contoso.com forest are federated.
The on-premises network contains a Hyper-V host that hosts a Red Hat Enterprise Linux virtual machine named Linux1. Linux1 has the following characteristics:
* A 2-TB disk
* Generation 1
* 10 virtual cores
* 128 GB of RAM
* A disk named LinuxVhd.vhdx
You plan to deploy infrastructure as a service (IaaS) to Dev for developer projects. The Marketplace on Dev is configured and ready to publish items. Dev contains one plan named Dev_Plan1 and one offer named Dev_Offer1.
Prod contains two plans and two offers. One of the offers is named Offer1.
All the IaaS and platform as a service (PaaS) tenant data must be backed up to an external location. The solution must ensure that the data can be restored if the datacenter that hosts Prod becomes unavailable.
End of repeated scenario.
You need to prepare a custom image based on Linux1 that will be deployed to Prod. The solution must prevent any changes to the current disk of Linux1.
Which four cmdlets should you run in sequence? To answer, move the appropriate cmdlets from the list of cmdlets to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
