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NEW QUESTION: 1
What is an example of a business architecture scenario?
A. Business architecture knowledgebase
B. Merger & acquisition
C. Balanced Scorecard
D. Strategy map
Answer: B
Explanation:
Topic2,Business Architecture Core Mapping Knowledge

NEW QUESTION: 2
Which command is most efficient in finding the pass4SymmKey of an index cluster?
A. find / -name server.conf -print | grep pass4SymKey
B. $SPLUNK_HOME/bin/splunk search | rest splunk_server=local /servicesNS/-/ unhash_app/storage/passwords
C. $SPLUNK_HOME/bin/splunk btool clustering list clustering --debug | grep pass4SymmKey
D. $SPLUNK_HOME/bin/splunk btool server list clustering | grep pass4SymmKey
Answer: C

NEW QUESTION: 3
Click on the exhibit.

Which of the following is FALSE?
A. The Isp-ping tool is used to perform a unidirectional LSP test.
B. The router can reach the far-end of the LSP through an RSVP-TE tunnel.
C. The router 10.10.10.2 is the egress router of the LSP.
D. The router can reach the far-end of the LSP through an LDP tunnel.
Answer: D

NEW QUESTION: 4
在庫レベルは、前日の最終在庫から当日の売上を差し引いて計算する必要があります。
Litwareに店舗と製品ごとに現在の在庫レベルを迅速に計算する機能を提供する2つのオプションはどれですか？それぞれの正解は完全な解決策を提示します。
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。
A. Event Hubs AvroファイルをAzure Blobストレージに出力します。 Azure Data Factoryのコピーアクティビティをトリガーして、10分ごとに実行し、データをAzure SQL Data Warehouseに読み込みます。 Transact-SQLを使用して、ストアおよび製品ごとにデータを集計します。
B. Event Hubs AvroファイルをAzure Blobストレージに出力します。 Transact-SQLを使用して、Azure SQL Data WarehouseのPolyBaseを使用して在庫レベルを計算します。
C. Azure Stream Analyticsを使用してイベントハブの出力を消費し、ストアおよび製品ごとにデータを集計します。結果のデータをAzure SQL Data Warehouseに直接出力します。 Transact-SQLを使用して、在庫レベルを計算します。
D. Azure Stream Analyticsを使用してイベントハブの出力を消費し、ストアおよび製品ごとにデータを集計します。結果のデータをDatabricksに出力します。 Databricksのインベントリレベルを計算し、データをAzure Blobストレージに出力します。
E. Databricksを使用して、イベントハブの出力を使用します。 Databricksを使用して在庫レベルを計算し、データをAzure SQL Data Warehouseに出力します。
Answer: A,C
Explanation:
A: Azure Stream Analytics is a fully managed service providing low-latency, highly available, scalable complex event processing over streaming data in the cloud. You can use your Azure SQL Data Warehouse database as an output sink for your Stream Analytics jobs.
E: Event Hubs Capture is the easiest way to get data into Azure. Using Azure Data Lake, Azure Data Factory, and Azure HDInsight, you can perform batch processing and other analytics using familiar tools and platforms of your choosing, at any scale you need.
Note: Event Hubs Capture creates files in Avro format.
Captured data is written in Apache Avro format: a compact, fast, binary format that provides rich data structures with inline schema. This format is widely used in the Hadoop ecosystem, Stream Analytics, and Azure Data Factory.
Scenario: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure.
Reference:
https://docs.microsoft.com/bs-latn-ba/azure/sql-data-warehouse/sql-data-warehouse-integrate-azure-stream-analytics
https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-capture-overview