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NEW QUESTION: 1
Note: This question is part of a series of questions that present the same Scenario.
Each question I 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 correct solution.
Start of repeated Scenario:
A Travel agency named Margie's Travel sells airline tickets to customers in the United
States.
Margie's Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure near about possible delays due to weather conditions.
The flight data contains the following attributes:
* DepartureDate: The departure date aggregated at a per hour granularity.
* Carrier: The code assigned by the IATA and commonly used to identify a carrier.
* OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight's Origin)
* DestAirportID: The departure delay in minutes.
*DepDet30: A Boolean value indicating whether the departure was delayed by 30 minutes or more ( a value of 1 indicates that the departure was delayed by 30 minutes or more)
The weather data contains the following Attributes: AirportID, ReadingDate (YYYY/MM/DD
HH), SKYConditionVisibility, WeatherType, Windspeed, StationPressure, PressureChange and HourlyPrecip.
End of repeated Scenario:
You need to use historical data about on-time flight performance and the weather data to predict whether the departure of a scheduled flight will be delayed by more than 30 minutes.
Which method should you use?
A. Classification
B. anomaly detection
C. Clustering
D. Linear regression
Answer: A
Explanation:
https://gallery.cortanaintelligence.com/Experiment/837e2095ce784f1ba5ac623a60232027
NEW QUESTION: 2
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、述べられた目標を達成する可能性のある独自の解決策が含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
AzureIoTハブから入力を受信して出力をAzureBlobストレージに送信するAzureStreamAnalyticsジョブがあります。このジョブには、互換性レベル1.1と6つのストリーミングユニットがあります。
ジョブに対して次のクエリがあります。
ストリーミングユニット数を12に増やす予定です。
追加のストリーミングユニットを利用してスループットを向上させるには、ジョブを最適化する必要があります。
解決策：ジョブの互換性レベルを1.2に変更します。
これは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
Max number of Streaming Units with one step and with no partitions is 6.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization
NEW QUESTION: 3
You use Azure Stream Analytics to receive Twitter data from Azure Event Hubs and to output the data to an Azure Blob storage account.
You need to output the count of tweets during the last five minutes every five minutes. Each tweet must only be counted once.
Which windowing function should you use?
A. a five-minute Hopping window that has one-minute hop
B. a five-minute Sliding window
C. a five-minute Session window
D. a five-minute Tumbling window
Answer: D
Explanation:
Explanation
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
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