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NEW QUESTION: 1
What are three volume-level storage efficiency features of ONTAP? (Choose three.)
A. deduplication
B. deswizzle
C. compression
D. space reclamation
E. compaction
Answer: A,B,C
Explanation:
DE: NetApp deduplication is an integral part of the NetApp Data ONTAP operating environment and the WAFL file system, which manages all data on NetApp storage systems.
NetApp data compression, introduced in Data ONTAP 8.0.1, is highly complementary to deduplication.
A: Data within a FlexVol has both a logical location within the volume and a physical location within the containing aggregate. When reading data from a FlexVol, the physical location of that data must be known to perform the read. Under normal circumstances, reads use a fast-path to translate a logical address into a physical address. When
SnapMirror transfers a FlexVol to a different aggregate, however, the relationship between logical and physical addresses is changed. The destination aggregate lays out the volume into different physical blocks. Immediately after the SnapMirror transfer, reads to the destination volume will use a different process to translate logical addresses into physical addresses. This slow-path is less efficient and may require an additional disk read to access a block of data. As soon as a FlexVol SnapMirror update finishes, the destination storage controller will launch a scanner to recreate the fast-path metadata. Once the scan finishes, reads to the SnapMirror destination will use the normal fast-path. This process is known as deswizzling.
References: http://community.netapp.com/t5/Tech-OnTap-Articles/Back-to-Basics-
Deduplication/ta-p/84888
https://kb.netapp.com/support/s/article/ka21A0000000arxQAA/what-is-deswizzler-or- deswizzling?language=en_US
NEW QUESTION: 2
ロール、ペルソナ、属性などのIDを承認にマッピングするコンセプトはどれですか。
A. 信頼できるソース
B. アクセス制御
C. フェデレーションID管理
D. 資格
E. 認証
Answer: D
NEW QUESTION: 3
A. Yes
B. No
Answer: B
Explanation:
Explanation
An Azure Blob Container, which uses an object store with flat namespace, is better than an Azure Data Lake Store, which uses Azure Data Lake uses an Hierarchical file system.
Scenario: Document Uploads
During the document upload process, the solution must capture information about the geographic location where documents originate. Processing of documents must be automatically triggered when documents are uploaded. Customers must be notified when analysis of their uploaded documents begins.
Uploaded documents must be processed using Azure Machine Learning Studio in an Azure Data Factory pipeline. The machine learning portion of the pipeline is updated once a quarter.
When document processing is complete, the documents and the results of the analysis process must be visible.
Reference:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-comparison-with-blob-storage
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