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NEW QUESTION: 1
A company runs an online marketplace web application on AWS. The application serves hundreds of thousands of users during peak hours. The company needs a scalable, near-real-time solution to share the details of millions of financial transactions with several other internal applications. Transactions also need to be processed to remove sensitive data before being stored in a document database for low-latency retrieval.
What should a solutions architect recommend to meet these requirements?
A. Stream the transactions data into Amazon Kinesis Data Firehose to store data in Amazon DynamoDB and Amazon S3. Use AWS Lambda integration with Kinesis Data Firehose to remove sensitive data. Other applications can consume the data stored in Amazon S3.
B. Stream the transactions data into Amazon Kinesis Data Streams. Use AWS Lambda integration to remove sensitive data from every transaction and then store the transactions data in Amazon DynamoDB. Other applications can consume the transactions data off the Kinesis data stream.
C. Store the batched transactions data in Amazon S3 as files. Use AWS Lambda to process every file and remove sensitive data before updating the files in Amazon S3. The Lambda function then stores the data in Amazon DynamoDB. Other applications can consume transaction files stored in Amazon S3.
D. Store the transactions data into Amazon DynamoDB. Set up a rule in DynamoDB to remove sensitive data from every transaction upon write. Use DynamoDB Streams to share the transactions data with other applications.
Answer: B

NEW QUESTION: 2
Note: The question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other question in the series. Information and details provided in a question apply only to that question.
You have a database named DB1. The database does not use a memory-optimized filegroup. The database contains a table named Table1. The table must support the following workloads:

You need to add the most efficient index to support the new OLTP workload, while not deteriorating the existing Reporting query performance.
What should you do?
A. Create a clustered columnstore index on the table.
B. Create a nonclustered columnstore index on the table.
C. Create a hash index on the table.
D. Create a nonclustered index on the table.
E. Create a nonclustered filtered index on the table.
F. Create a clustered index on the table.
Answer: E
Explanation:
Explanation/Reference:
Explanation:
A filtered index is an optimized nonclustered index, especially suited to cover queries that select from a well-defined subset of data. It uses a filter predicate to index a portion of rows in the table. A well-designed filtered index can improve query performance, reduce index maintenance costs, and reduce index storage costs compared with full-table indexes.
References:https://technet.microsoft.com/en-us/library/cc280372(v=sql.105).aspx

NEW QUESTION: 3
PostgreSQL用Amazon RDSでは、データベースインスタンスごとに最大3TBのストレージと30,000 IOPSをプロビジョニングできます。
cr1.8xlargeインスタンスで50％の書き込みと50％の読み取りを実行するワークロードの場合、PostgreSQLで25,000を超えるIOPSを実現できます。ただし、この制限を超えてプロビジョニングすることにより、次のことを実現できる場合があります。
A. より高いレイテンシーとより低いスループット。
B. レイテンシーのみ。
C. 待ち時間が短く、スループットが高い。
D. より高いスループットのみ。
Answer: C
Explanation:
Explanation
You can provision up to 3TB storage and 30,000 IOPS per database instance. For a workload with 50% writes and 50% reads running on a cr1.8xlarge instance, you can realize over 25,000 IOPS for PostgreSQL. However, by provisioning more than this limit, you may be able to achieve lower latency and higher throughput. Your actual realized IOPS may vary from the amount you provisioned based on your database workload, instance type, and database engine choice.
https://aws.amazon.com/rds/postgresql/

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