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NEW QUESTION: 1
You installed redundant power supplies of different capacities into your EX switch. In this scenario, which statement is true?
A. The POE power budget is based on the combined wattage of both power supplies
B. The POE power budget is based on the wattage of the higher capacity power supply.
C. The POE power budget is based on the number of ports on the switch
D. The POE power budget is based on the wattage of the lower capacity power supply
Answer: D
NEW QUESTION: 2
Microsoft AzureでLambdaアーキテクチャを使用してソリューションを開発しています。
テストレイヤーのデータは、次の要件を満たしている必要があります。
データストレージ：
*リポジトリ（またはさまざまな形式の大量の大容量ファイル）として機能します。
*ビッグデータ分析ワークロード用に最適化されたストレージを実装します。
*階層構造を使用してデータを整理できるようにします。
バッチ処理：
*メモリ内の計算処理にはマネージドソリューションを使用します。
* Scala、Python、およびRプログラミング言語をネイティブにサポートします。
*クラスターを自動的にサイズ変更および終了する機能を提供します。
Analytical data store:
* Support parallel processing.
* Use columnar storage.
* Support SQL-based languages.
Lambdaアーキテクチャを構築するには、正しいテクノロジーを特定する必要があります。
どのテクノロジーを使用する必要がありますか？回答するには、回答エリアで適切なオプションを選択します
注：それぞれの正しい選択には1ポイントの価値があります。
Answer:
Explanation:
Explanation
Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL
Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is
NEW QUESTION: 3
A security practitioner is tasked with securing the organization's Wireless Access Points (WAP). Which of these is the MOST effective way of restricting this environment to authorized users?
A. Change the name of the Service Set Identifier (SSID) to a random value not associated with the organization
B. Enable Wi-Fi Protected Access 2 (WPA2) encryption on the wireless access point
C. Create Access Control Lists (ACL) based on Media Access Control (MAC) addresses
D. Disable the broadcast of the Service Set Identifier (SSID) name
Answer: C
NEW QUESTION: 4
Your database contains a table named Purchases. The table includes a DATETIME column named PurchaseTime that stores the date and time each purchase is made. There is a non-clustered index on the PurchaseTime column.
The business team wants a report that displays the total number of purchases made on the current day.
You need to write a query that will return the correct results in the most efficient manner.
Which Transact-SQL query should you use?
A. SELECT COUNT(*)
FROM Purchases
WHERE PurchaseTime = GETDATE()
B. SELECT COUNT(*)
FROM Purchases
WHERE PurchaseTime = CONVERT(DATE, GETDATE())
C. SELECT COUNT(*)
FROM Purchases
WHERE PurchaseTime >= CONVERT(DATE, GETDATE())
AND PurchaseTime < DATEADD(DAY, 1, CONVERT(DATE, GETDATE()))
D. SELECT COUNT(*)
FROM Purchases
WHERE CONVERT(VARCHAR, PurchaseTime, 112) = CONVERT(VARCHAR, GETDATE(), 112)
Answer: C
Explanation:
Two answers will return the correct results (the "WHERE CONVERT..." and "WHERE ... AND ... " answers).
The correct answer for Microsoft would be the answer that is most "efficient". Anybody have a clue as to which is most efficient? In the execution plan, the one that I've selected as the correct answer is the query with the shortest duration. Also, the query answer with "WHERE CONVERT..." threw warnings in the execution plan...something about affecting CardinalityEstimate and SeekPlan.
I also found this article, which leads me to believe that I have the correct answer:
http://technet.microsoft.com/en-us/library/ms181034.aspx