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NEW QUESTION: 1
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.
You have five servers that run Microsoft Windows 2012 R2. Each server hosts a Microsoft SQL Server instance. The topology for the environment is shown in the following diagram.

You have an Always On Availability group named AG1. The details for AG1 are shown in the following table.

Instance1 experiences heavy read-write traffic. The instance hosts a database named OperationsMain that is four terabytes (TB) in size. The database has multiple data files and filegroups. One of the filegroups is read_only and is half of the total database size.
Instance4 and Instance5 are not part of AG1. Instance4 is engaged in heavy read-write I/O.
Instance5 hosts a database named StagedExternal. A nightly BULK INSERT process loads data into an empty table that has a rowstore clustered index and two nonclustered rowstore indexes.
You must minimize the growth of the StagedExternal database log file during the BULK INSERT operations and perform point-in-time recovery after the BULK INSERT transaction. Changes made must not interrupt the log backup chain.
You plan to add a new instance named Instance6 to a datacenter that is geographically distant from Site1 and Site2. You must minimize latency between the nodes in AG1.
All databases use the full recovery model. All backups are written to the network location \\SQLBackup\. A separate process copies backups to an offsite location. You should minimize both the time required to restore the databases and the space required to store backups. The recovery point objective (RPO) for each instance is shown in the following table.

Full backups of OperationsMain take longer than six hours to complete. All SQL Server backups use the keyword COMPRESSION.
You plan to deploy the following solutions to the environment. The solutions will access a database named DB1 that is part of AG1.
* Reporting system: This solution accesses data inDB1with a login that is mapped to a database user that is a member of the db_datareader role. The user has EXECUTE permissions on the database. Queries make no changes to the data. The queries must be load balanced over variable read-only replicas.
* Operations system: This solution accesses data inDB1with a login that is mapped to a database user that is a member of the db_datareader and db_datawriter roles. The user has EXECUTE permissions on the database. Queries from the operations system will perform both DDL and DML operations.
The wait statistics monitoring requirements for the instances are described in the following table.

You need to analyze the wait type and statistics for specific instanced in the environment.
Which object should you use to gather information about each instance? To answer, drag the appropriate objects to the correct instances. Each object may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation
Instance 1: sys.dm_exec_query_stats
From Scenario: Instance1 requirement: Aggregate statistics since last server restart.
sys.dm_exec_query_stats returns aggregate performance statistics for cachedquery plans in SQL Server.

Instance 4: sys.dm_os_wait_stats
sys.dm_os_wait_statsreturns information about all the waits encountered by threads that executed.
From Scenario: Instance4 requirement: Identify the most prominent wait types.

Instance 5:sys.dm_exec_session_wait_stats
From Scenario: Instance5 requirement: Identify all wait types for queries currently running on the server.
sys.dm_exec_session_wait_stats returns information about all the waits encountered by threads that executed for each session.

NEW QUESTION: 2

A. Option A
B. Option D
C. Option B
D. Option C
Answer: C,D
Explanation:
Explanation
The various client libraries define classes like Index, Field, and Document, as well as operations like Indexes.Create and Documents.Search on the SearchServiceClient and SearchIndexClient classes.
Example:
The sample application we'll be exploring creates a new index named "hotels", populates it with a few documents, then executes some search queries. Here is the main program, showing the overall flow:
/ This sample shows how to delete, create, upload documents and query an index static void Main(string[] args)
{
IConfigurationBuilder builder = new ConfigurationBuilder().AddJsonFile("appsettings.json"); IConfigurationRoot configuration = builder.Build(); SearchServiceClient serviceClient = CreateSearchServiceClient(configuration); Console.WriteLine("{0}", "Deleting index...\n"); DeleteHotelsIndexIfExists(serviceClient); Console.WriteLine("{0}", "Creating index...\n"); CreateHotelsIndex(serviceClient); ISearchIndexClient indexClient = serviceClient.Indexes.GetClient("hotels");
References:
https://docs.microsoft.com/en-us/azure/search/search-howto-dotnet-sdk

NEW QUESTION: 3
You are designing a solution that will use Apache HBase on Microsoft Azure HDInsight.
You need to design the row keys for the database to ensure that client traffic is directed over all of the nodes in the cluster.
What are two possible techniques that you can use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. padding
B. salting
C. hashing
D. trimming
Answer: B,C
Explanation:
Explanation/Reference:
Explanation:
There are two strategies that you can use to avoid hotspotting:
* Hashing keys
To spread write and insert activity across the cluster, you can randomize sequentially generated keys by hashing the keys, inverting the byte order. Note that these strategies come with trade-offs. Hashing keys, for example, makes table scans for key subranges inefficient, since the subrange is spread across the cluster.
* Salting keys
Instead of hashing the key, you can salt the key by prepending a few bytes of the hash of the key to the actual key.
Note. Salted Apache HBase tables with pre-split is a proven effective HBase solution to provide uniform workload distribution across RegionServers and prevent hot spots during bulk writes. In this design, a row key is made with a logical key plus salt at the beginning. One way of generating salt is by calculating n (number of regions) modulo on the hash code of the logical row key (date, etc).
Reference:
https://blog.cloudera.com/blog/2015/06/how-to-scan-salted-apache-hbase-tables-with-region-specific-key- ranges-in-mapreduce/
http://maprdocs.mapr.com/51/MapR-DB/designing_row_keys_for_mapr_db_binary_tables.html

NEW QUESTION: 4
Which of the following is ensured when hashing files during chain of custody handling?
A. Availability
B. Integrity
C. Non-repudiation
D. Accountability
Answer: B