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NEW QUESTION: 1
A. Memory reservation
B. Expandable reservation
C. Unlimited memory
D. High memory share value
Answer: A
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 can skip the network by executing against near caches directly, whereas Aggregators must execute on storage members.
C. Entry Processors require a key or a set of keys, whereas Aggregators require filters.
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. はい
B. いいえ
Answer: B
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