Microsoft Developing SQL Databases (70-762日本語版) - 70-762日本語 Exam Questions

Explanation

References: https://technet.microsoft.com/en-us/library/ms189294(v=sql.105).aspx

Explanation

Read Uncommitted (aka dirty read): A transaction T1executing under this isolation level can access data changed by concurrent transaction(s).
Pros:No read locks needed to read data (i.e. no reader/writer blocking). Note, T1 still takes transaction duration locks for any data modified.
Cons: Data is not guaranteed to be transactionally consistent.
Read Committed: A transaction T1 executing under this isolation level can only access committed data.
Pros: Good compromise between concurrency and consistency.
Cons: Locking and blocking. The data can change when accessed multiple times within the same transaction.
Repeatable Read: A transaction T1 executing under this isolation level can only access committed data with an additional guarantee that any data read cannot change (i.e. it is repeatable) for the duration of the transaction.
Pros: Higher data consistency.
Cons: Locking and blocking. The S locks are held for the duration of the transaction that can lower the concurrency. It does not protect against phantom rows.
Serializable: A transaction T1 executing under this isolation level provides the highest data consistency including elimination of phantoms but at the cost of reduced concurrency. It prevents phantoms by taking a range lock or table level lock if range lock can't be acquired (i.e. no index on the predicate column) for the duration of the transaction.
Pros: Full data consistency including phantom protection.
Cons: Locking and blocking. The S locks are held for the duration of the transaction that can lower the concurrency.
References:
https://blogs.msdn.microsoft.com/sqlcat/2011/02/20/concurrency-series-basics-of-transaction-isolation-levels/

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Explanation

References: https://msdn.microsoft.com/en-us/library/ms186736.aspx

Explanation

Box 1:
Specifying the order in which key values are stored in an index is useful when queries referencing the table have ORDER BY clauses that specify different directions for the key column or columns in that index. In these cases, the index can remove the need for a SORT operator in the query plan; therefore, this makes the query more efficient.
The Database Engine can move equally efficiently in either direction. An index defined as (RejectedQty DESC, ProductID ASC) can still be used for a query in which the sort direction of the columns in the ORDER BY clause are reversed. For example, a query with the ORDER BY clause ORDER BY RejectedQty ASC, ProductID DESC can use the index.
Box 2:
You can include nonkey columns in a nonclustered index to avoid exceeding the current index size limitations of a maximum of 16 key columns and a maximum index key size of 900 bytes. The Database Engine does not consider nonkey columns when calculating the number of index key columns or index key size.
References: https://technet.microsoft.com/en-us/library/jj835095(v=sql.110).aspx

Explanation

Box 1: sys.dm_exec_query_stats
sys.dm_exec_query_stats returns aggregate performance statistics for cached query plans in SQL Server.
Box 2: highest_cpu_queries.total_worker_time DESC
Sort on total_worker_time column
Example: The following example returns information about the top five queries ranked by average CPU time.
This example aggregates the queries according to their query hash so that logically equivalent queries are grouped by their cumulative resource consumption.
USE AdventureWorks2012;
GO
SELECT TOP 5 query_stats.query_hash AS "Query Hash",
SUM(query_stats.total_worker_time) / SUM(query_stats.execution_count) AS "Avg CPU Time", MIN(query_stats.statement_text) AS "Statement Text" FROM (SELECT QS.*, SUBSTRING(ST.text, (QS.statement_start_offset/2) + 1, ((CASE statement_end_offset WHEN -1 THEN DATALENGTH(ST.text) ELSE QS.statement_end_offset END
- QS.statement_start_offset)/2) + 1) AS statement_text
FROM sys.dm_exec_query_stats AS QS
CROSS APPLY sys.dm_exec_sql_text(QS.sql_handle)as ST) as query_stats
GROUP BY query_stats.query_hash
ORDER BY 2 DESC;
References: https://msdn.microsoft.com/en-us/library/ms189741.aspx

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Explanation

Box 1: Durable in-Memory OLTP
Databases containing memory-optimized tables, with or without native compiled stored procedures, are fully supported with Always On Availability Groups. There is no difference in the configuration and support for databases which contain In-Memory OLTP objects as compared to those without.
Box 2: Always on Availability groups
The Always On availability groups feature is a high-availability and disaster-recovery solution that provides an enterprise-level alternative to database mirroring. Always On availability groups maximizes the availability of a set of user databases for an enterprise. An availability group supports a failover environment for a discrete set of user databases, known as availability databases, that fail over together. An availability group supports a set of read-write primary databases and one to eight sets of corresponding secondary databases. Optionally, secondary databases can be made available for read-only access and/or some backup operations.
An availability group fails over at the level of an availability replica.
References:
https://docs.microsoft.com/en-us/sql/relational-databases/in-memory-oltp/high-availability-support-for-in-memo
https://docs.microsoft.com/en-us/sql/database-engine/availability-groups/windows/always-on-availability-groups