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NEW QUESTION: 1

A. FC SAN
B. iSCSI
C. FCoE
D. JBOD
Answer: A

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、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。
Face APIを使用するApp1という名前のアプリがあります。
App1には複数のPersonGroupオブジェクトが含まれています。
Ben Smithという名前の個人のPersonGroupオブジェクトは、追加のエントリを受け入れることができないことがわかります。 Ben SmithのPersonGroupオブジェクトには10​​,000エントリが含まれています。
Ben SmithのPersonGroupオブジェクトに追加のエントリを追加できるようにする必要があります。ソリューションでは、すべてのエントリでBen Smithを識別できるようにする必要があります。
解決策：App1のトレーニングフェーズのカスタム時間間隔を変更します。
これは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
Explanation
Instead, use a LargePersonGroup. LargePersonGroup and LargeFaceList are collectively referred to as large-scale operations. LargePersonGroup can contain up to 1 million persons, each with a maximum of 248 faces. LargeFaceList can contain up to 1 million faces. The large-scale operations are similar to the conventional PersonGroup and FaceList but have some differences because of the new architecture.
References:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/how-to-use-large-scale

NEW QUESTION: 3
Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the two tables are formatted as comma-separated files in HDFS.
A. Yes, so long as both tables fit into memory.
B. No, MapReduce cannot perform relational operations.
C. No, but it can be done with either Pig or Hive.
D. Yes.
E. Yes, but only if one of the tables fits into memory
Answer: D
Explanation:
Note:
*Join Algorithms in MapReduce A) Reduce-side join B) Map-side join C) In-memory join / Striped Striped variant variant / Memcached variant
*Which join to use? / In-memory join > map-side join > reduce-side join / Limitations of each? In-memory join: memory Map-side join: sort order and partitioning Reduce-side join: general purpose