A banking company wants to collect large volumes of transactional data using Amazon Kinesis Data Streams for real-time analytics. The company uses PutRecord to send data to Amazon Kinesis, and has observed network outages during certain times of the day. The company wants to obtain exactly once semantics for the entire processing pipeline.
What should the company do to obtain these characteristics?

• A.Rely on the exactly one processing semantics of Apache Flink and Apache Spark Streaming included in Amazon EMR.
• B.Rely on the processing semantics of Amazon Kinesis Data Analytics to avoid duplicate processing of events.
• C.Design the application so it can remove duplicates during processing be embedding a unique ID in each record.
• D.Design the data producer so events are not ingested into Kinesis Data Streams multiple times.

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A data analyst is designing an Amazon QuickSight dashboard using centralized sales data that resides in Amazon Redshift. The dashboard must be restricted so that a salesperson in Sydney, Australia, can see only the Australia view and that a salesperson in New York can see only United States (US) data.
What should the data analyst do to ensure the appropriate data security is in place?

• A.Deploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.
• B.Place the data sources for Australia and the US into separate SPICE capacity pools.
• C.Set up an Amazon Redshift VPC security group for Australia and the US.
• D.Deploy QuickSight Enterprise edition to implement row-level security (RLS) to the sales table.

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A large energy company is using Amazon QuickSight to build dashboards and report the historical usage data of its customers This data is hosted in Amazon Redshift The reports need access to all the fact tables' billions ot records to create aggregation in real time grouping by multiple dimensions A data analyst created the dataset in QuickSight by using a SQL query and not SPICE Business users have noted that the response time is not fast enough to meet their needs Which action would speed up the response time for the reports with the LEAST implementation effort?

• A.Use Amazon Redshift to create a materialized view that joins the fact table with the dimensions
• B.Use QuickSight to modify the current dataset to use SPICE
• C.Use Amazon Redshift to create a stored procedure that joins the fact table with the dimensions Load the data into a new table
• D.Use AWS Glue to create an Apache Spark job that joins the fact table with the dimensions. Load the data into a new table

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A company leverages Amazon Athena for ad-hoc queries against data stored in Amazon S3. The company wants to implement additional controls to separate query execution and query history among users, teams, or applications running in the same AWS account to comply with internal security policies.
Which solution meets these requirements?

• A.Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM users. and apply the S3 bucket policy to the S3 bucket.
• B.Create an Athena workgroup for each given use case, apply tags to the workgroup, and create an IAM policy using the tags to apply appropriate permissions to the workgroup.
• C.Create an IAM role for each given use case, assign appropriate permissions to the role for the given use case, and add the role to associate the role with Athena.
• D.Create an AWS Glue Data Catalog resource policy for each given use case that grants permissions to appropriate individual IAM users, and apply the resource policy to the specific tables used by Athena.

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A company needs to store objects containing log data in JSON format. The objects are generated by eight applications running in AWS. Six of the applications generate a total of 500 KiB of data per second, and two of the applications can generate up to 2 MiB of data per second.
A data engineer wants to implement a scalable solution to capture and store usage data in an Amazon S3 bucket. The usage data objects need to be reformatted, converted to .csv format, and then compressed before they are stored in Amazon S3. The company requires the solution to include the least custom code possible and has authorized the data engineer to request a service quota increase if needed.
Which solution meets these requirements?

• A.Configure an Amazon Kinesis data stream for each application. Write an AWS Lambda function to read usage data objects from the stream for each application. Have the function perform .csv conversion, reformatting, and compression of the data. Have the function store the output in Amazon S3.
• B.Configure an Amazon Kinesis Data Firehose delivery stream for each application. Write AWS Lambda functions to read log data objects from the stream for each application. Have the function perform reformatting and .csv conversion. Enable compression on all the delivery streams.
• C.Configure an Amazon Kinesis data stream with one shard per application. Write an AWS Lambda function to read usage data objects from the shards. Have the function perform .csv conversion, reformatting, and compression of the data. Have the function store the output in Amazon S3.
• D.Store usage data objects in an Amazon DynamoDB table. Configure a DynamoDB stream to copy the objects to an S3 bucket. Configure an AWS Lambda function to be triggered when objects are written to the S3 bucket. Have the function convert the objects into .csv format.

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