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NEW QUESTION: 1
A media company has a 30-TB repository of digital news videos. These videos are stored on tape in an on-premises tape library and referenced by a Media Asset Management (MAM) system.
The company wants to enrich the metadata for these videos in an automated fashion and put them into a searchable catalog by using a MAM feature. The company must be able to search based on information in the video, such as objects, scenery items, or people's faces. A catalog is available that contains faces of people who have appeared in the videos that include an image of each person. The company would like to migrate these videos to AWS.
The company has a high-speed AWS Direct Connect connection with AWS and would like to move the MAM solution video content directly from its current file system.
How can these requirements be met by using the LEAST amount of ongoing management overhead and causing MINIMAL disruption to the existing system?
A. Set up an Amazon EC2 instance that runs the OpenCV libraries. Copy the videos, images, and face catalog from the one-premises library into an Amazon EBS volume mounted on this EC2 instance.
Process the videos to retrieve the required metadata, and push the metadata into the MAM solution while also copying the video files to an Amazon S3 bucket.
B. Configure a video ingestion stream by using Amazon Kinesis Video Streams. Use the catalog of faces to build a collection in Amazon Rekognition. Stream the videos from the MAM solution into Kinesis Video Streams. Configure Amazon Rekognition to process the streamed videos. Then, use a stream consumer to retrieve the required metadata, and push the metadata into the MAM solution. Configure the stream to store the videos in Amazon S3.
C. Set up an AWS Storage Gateway, tape gateway appliance on-premises. Use the MAM solution to extract the videos from the current archive and push them into the tape gateway. Use the catalog of faces to build a collection in Amazon Rekognition. Build an AWS Lambda function that invokes the Rekognition Javascript SDK to have Amazon Rekognition process the video in the tape gateway, retrieve the required metadata, and push the metadata into the MAM solution.
D. Set up an AWS Storage Gateway, file gateway appliance on premises. Use the MAM solution to extract the videos from the current archive and push them into the file gateway. Use the catalog of faces to build a collection in Amazon Rekognition. Build an AWS Lambda function that invokes the Rekognition Javascript SDK to have Rekognition pull the video from the Amazon S3 files backing the file gateway, retrieve the required metadata, and push the metadata into the MAM solution.
Answer: D
Explanation:
A\B: By replacing the physical tape library with file gateway, it has the least amount of management and disruption. It would require us to restore the 30TB to another format (NFS\SMB style) in order to put it on the file gateway which cannot serve as a VTL. I don't think it's possible for Amazon Rekognition to process the video in the tape gateway which is on premise or access the data from S3 directly.
A: File gateway supports Linux clients connecting to the gateway using Network File System (NFS) versions 3 and 4.1 for Linux clients, and supports Windows clients connecting to the gateway using Server Message Block (SMB) versions 2 and 3.
B: No. You cannot access virtual tape data using Amazon S3 or Amazon S3 Glacier APIs.
However, you can use the tape gateway APIs to manage your virtual tape library and your virtual tape shelf.
https://aws.amazon.com/storagegateway/faqs/?nc=sn&loc=6
C: This is not possible because the data are in tape format.
D: This loosk more like the job for Rekognition.

NEW QUESTION: 2
アルゴリズムのハイパーパラメーターを調整しています。 次の表は、さまざまなハイパーパラメーター、トレーニングエラー、および検証エラーを含むデータセットを示しています。

ドロップダウンメニューを使用して、グラフィックに表示される情報に基づいて各質問に回答する回答選択肢を選択します。

Answer:
Explanation:

Explanation:
Box 1: 4
Choose the one which has lower training and validation error and also the closest match.
Minimize variance (difference between validation error and train error).
Box 2: 5
Minimize variance (difference between validation error and train error).
Reference:
https://medium.com/comet-ml/organizing-machine-learning-projects-project-management-guidelines-2d2b85651bbd

NEW QUESTION: 3
北米に拠点を置く会社。ヨーロッパとアジアは、グローバルサプライチェーンと製造プロセスを最適化するために、新しい分散アプリケーションを設計しています。 1つの大陸で予約された注文は、1秒以内にすべてのリージョンに表示されます。データベースは、短い目標復旧時間（RTO）でフェイルオーバーをサポートできる必要があります。アプリケーションの稼働時間は、製造に影響が及ばないようにするために重要です。ソリューションアーキテクトが推奨することは何ですか。
A. クロスリージョンリードレプリカでAmazon RDS for MySQLを使用します
B. Amazon Auroraグローバルデータベースを使用
C. Amazon DynamoDBグローバルテーブルを使用する
D. クロスリージョンのリードレプリカでAmazon RDS for PostgreSQLを使用します
Answer: B
Explanation:
Cross-Region Disaster Recovery
If your primary region suffers a performance degradation or outage, you can promote one of the secondary regions to take read/write responsibilities. An Aurora cluster can recover in less than 1 minute even in the event of a complete regional outage. This provides your application with an effective Recovery Point Objective (RPO) of 1 second and a Recovery Time Objective (RTO) of less than 1 minute, providing a strong foundation for a global business continuity plan.

NEW QUESTION: 4
Refer to the exhibit.

An administrator must configure a SAN port channel between a cisco UCS and a MDS switch as shown in the exhibit.
The UCS fabric interconnects are in FC switch mode. The administrator creates a port channel on the MDS switches with the configuration. After, which configuration change is needed to bring up the port channel?
A. feature npv configured on MDS
B. switchport mode E on the port-channel interface
C. Cisco UCS port configured as storage
D. feature port-channel configured on mds
E. switchport mode NP on the port-channel interface
F. Cisco UCS port configured as FCoE uplink
Answer: B