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NEW QUESTION: 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are tuning the performance of a virtual machine that hosts a Microsoft SQL Server instance.
The virtual machine originally had four CPU cores and now has 32 CPU cores.
The SQL Server instance uses the default settings and has an OLTP database named db1. The largest table in db1 is a key value store table named table1.
Several reports use the PIVOT statement and access more than 100 million rows in table1.
You discover that when the reports run, there are PAGELATCH_IO waits on PFS pages 2:1:1, 2:2:1, 2:3:1, and 2:4:1 within the tempdb database.
You need to prevent the PAGELATCH_IO waits from occurring.
Solution: You add more tempdb databases.
Does this meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Explanation/Reference:
Explanation:
From SQL Server's perspective, you can measure the I/O latency from sys.dm_os_wait_stats. If you consistently see high waiting for PAGELATCH_IO, you can benefit from a faster I/O subsystem for SQL Server.
A cause can be poor design of your database - you may wish to split out data located on 'hot pages', which are accessed frequently and which you might identify as the causes of your latch contention. For example, if you have a currency table with a data page containing 100 rows, of which 1 is updated per transaction and you have a transaction rate of 200/sec, you could see page latch queues of 100 or more. If each page latch wait costs just 5ms before clearing, this represents a full half-second delay for each update. In this case, splitting out the currency rows into different tables might prove more performant (if less normalized and logically structured).
References: https://www.mssqltips.com/sqlservertip/3088/explanation-of-sql-server-io-and-latches/
NEW QUESTION: 2
A large manufacturing company has manufacturing plants in many states. Each state has a dedicated SQL Server instance that stores its manufacturing data. Each SQL Server instance is configured identically and all instances have identical database structures.
You are developing a daily report that summarizes information on manufacturing activity.
The report has the following requirements:
It must have a state name as one of the report parameters.
It must provide a daily summary of manufacturing activity of a selected state.
It must require minimal development and maintenance effort.
You need to develop the report to meet the requirements.
What should you do? (More than one answer choice may achieve the goal. Select the BEST answer.)
A. Use a shared data source that defines an expression-based connection string based on the state parameter.
B. Build one report for each state and instruct users to execute reports as needed.
C. Use a report-specific data source that defines an expression-based connection string based on the state parameter.
D. Build a centralized data repository, schedule a regular Extract, Transform, and Load
---
(ETL) process on all manufacturing data, and then use the repository to generate the report.
Answer: C
NEW QUESTION: 3
Einstein Discoveryの場合：
A. 「予測と改善」の洞察は、将来何が起こる可能性があるかを調べるのに役立ちます。たとえば、「ストーリーのwhat if分析。EinsteinDiscoveryは、データセットの統計分析と予測分析に基づいた予測と改善案を提供します。これらの洞察を視覚化するために、Einstein Discoveryは以下を使用します。
-予測用のウォーターフォールチャート
-改善案の棒グラフ
B. 「違いとは」洞察は、ストーリーの説明変数と目標（目標結果変数）の関係をよりよく理解するのに役立つ比較洞察です。これらの洞察は、データセットの統計分析に基づいており、結果変数の最大の変化に寄与する要因を把握するのに役立ちます。 Einstein Discoveryは、ウォーターフォールチャートを使用して、What Is The Differenceインサイトで比較を視覚化するのに役立ちます。
C. 「なぜ起こったのか」という洞察は、結果につながった正確な要因をより深く調べるのに役立ちます。
なぜそれが起こったのかs / Qの洞察は、ストーリーの目標に貢献したさまざまな要因をより深く掘り下げます。
これらの洞察は、データセットの統計分析に基づいています。 Einstein Discoveryは、ウォーターフォールチャートを使用して、なぜそれが起こったのかを洞察するのに役立ちます。
D. 「何が起こったのか」という洞察は、ストーリーの主要な洞察です。これらは、データセットの統計分析に基づいて、y / Qの概要レベルで結果に貢献した要因を調査するのに役立つ説明的な洞察です。 .Einstein Discoveryは、棒グラフを使用して、Happened What insightsの視覚化を支援します。
Answer: A,B,D
NEW QUESTION: 4
In the smart solid area comprehensive security environment, Huawei's main promotion models with situation monitoring include ()
A. M
B. X
C. IPC
D. C
Answer: A,B,D