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NEW QUESTION: 1
A. High memory share value
B. Memory reservation
C. Expandable reservation
D. Unlimited memory
Answer: B
NEW QUESTION: 2
What is the difference between Entry Processors and Aggregators?
A. Entry Processors are better suited to Map-Reduce operations than Aggregators.
B. Entry Processors require a key or a set of keys, whereas Aggregators require filters.
C. Entry Processors can skip the network by executing against near caches directly, whereas Aggregators must execute on storage members.
D. Aggregators are optimized for read-only access, whereas Entry Processors can modify cache entries.
Answer: D
Explanation:
Explanation/Reference:
Reference: https://www.c2b2.co.uk/middleware-blog/do-coherence-aggregators-failover.php
NEW QUESTION: 3
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、記載された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
質問に答えた後、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。
ストリーミングデータを生成するAzure IoT Edgeデバイスがあります。
デバイスでは、Azure Machine Learningモデルを使用してデータの異常を検出する必要があります。異常が検出されたら、デバイスは異常に関する情報をAzure IoT Hubストリームに追加する必要があります。
解決策：Machine LearningモデルをAzure Webサービスとして公開します。
これは目標を達成していますか？
A. いいえ
B. はい
Answer: A
Explanation:
Explanation
Instead use Azure Stream Analytics and REST API.
Note. Available in both the cloud and Azure IoT Edge, Azure Stream Analytics offers built-in machine learning based anomaly detection capabilities that can be used to monitor the two most commonly occurring anomalies:
temporary and persistent.
Stream Analytics supports user-defined functions, via REST API, that call out to Azure Machine Learning endpoints.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-machine-learning-anomaly-detection