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NEW QUESTION: 1

A. Option A
B. Option D
C. Option C
D. Option B
Answer: A,C

NEW QUESTION: 2
Which of the following devices uses both light and a lens to facilitate data warehousing?
A. Camera
B. Digitizer
C. Barcode reader
D. Touch screen
Answer: C

NEW QUESTION: 3
ピーク時以外の時間帯に、開発者は通常のワークロードに影響を与えずに、完全なAmazon DynamoDBテーブルスキャンの実行時間を最小限に抑えることを望みます。ワークロードは、ピーク時以外の時間帯に一貫性のある読み取りキャパシティーユニットの半分を平均します。
開発者はこのスキャンをどのように最適化しますか？
A. レートを制限しながらパラレルスキャンを使用します
B. 順次スキャンを使用する
C. スキャン動作中に読み取り容量の単位を増やします
D. スキャン操作中に整合性を最終的に整合性のあるものに変更します
Answer: A
Explanation:
Explanation
Parallel scans would help improve the scan performance while using limit parameter the provisioned throughput consumed can be limited.
Refer AWS documentation - DynamoDB Parallel Scan
By default, the Scan operation processes data sequentially. DynamoDB returns data to the application in 1 MB increments, and an application performs additional operations to retrieve the next 1 MB of data.
The larger the table or index being scanned, the more time the will take to complete. In addition, a sequential Scan might not always be able to fully utilize the provisioned read throughput capacity: Even though DynamoDB distributes a large table s data across multiple physical partitions, a Scan operation can only read one partition at a time. For this reason, the throughput of a is constrained by the maximum throughput of a single partition.
To address these issues, the Scan operation can logically divide a table or secondary index into multiple segments, with multiple application workers scanning the segments in parallel. Each worker can be a thread (in programming languages that support multithreading) or an operating system process.
A parallel scan with a large number of workers can easily consume all of the provisioned throughput for the table or index being scanned. It is best to avoid such scans if the table or index is also incurring heavy read or write activity from other applications.
To control the amount of data returned per request, use the Limit parameter. This can help prevent situations where one worker consumes all of the provisioned throughput, at the expense of all other workers.