A retail company's data analytics team recently created multiple product sales analysis dashboards for the average selling price per product using Amazon QuickSight. The dashboards were created from .csv files uploaded to Amazon S3. The team is now planning to share the dashboards with the respective external product owners by creating individual users in Amazon QuickSight. For compliance and governance reasons, restricting access is a key requirement. The product owners should view only their respective product analysis in the dashboard reports.
Which approach should the data analytics team take to allow product owners to view only their products in the dashboard?

• A.Separate the data by product and use S3 bucket policies for authorization.
• B.Separate the data by product and use IAM policies for authorization.
• C.Create a manifest file with row-level security.
• D.Create dataset rules with row-level security.

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A bank operates in a regulated environment. The compliance requirements for the country in which the bank operates say that customer data for each state should only be accessible by the bank's employees located in the same state. Bank employees in one state should NOT be able to access data for customers who have provided a home address in a different state.
The bank's marketing team has hired a data analyst to gather insights from customer data for a new campaign being launched in certain states. Currently, data linking each customer account to its home state is stored in a tabular .csv file within a single Amazon S3 folder in a private S3 bucket. The total size of the S3 folder is 2 GB uncompressed. Due to the country's compliance requirements, the marketing team is not able to access this folder.
The data analyst is responsible for ensuring that the marketing team gets one-time access to customer data for their campaign analytics project, while being subject to all the compliance requirements and controls.
Which solution should the data analyst implement to meet the desired requirements with the LEAST amount of setup effort?

• A.Re-arrange data in Amazon S3 to store customer data about each state in a different S3 folder within the same bucket. Set up S3 bucket policies to provide marketing employees with appropriate data access under compliance controls. Delete the bucket policies after the project.
• B.Load tabular data from Amazon S3 to Amazon QuickSight Enterprise edition by directly importing it as a data source. Use the built-in row-level security feature in Amazon QuickSight to provide marketing employees with appropriate data access under compliance controls. Delete Amazon QuickSight data sources after the project is complete.
• C.Load tabular data from Amazon S3 to Amazon Redshift with the COPY command. Use the built-in row- level security feature in Amazon Redshift to provide marketing employees with appropriate data access under compliance controls. Delete the Amazon Redshift tables after the project.
• D.Load tabular data from Amazon S3 to an Amazon EMR cluster using s3DistCp. Implement a custom Hadoop-based row-level security solution on the Hadoop Distributed File System (HDFS) to provide marketing employees with appropriate data access under compliance controls. Terminate the EMR cluster after the project.

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A company has 1 million scanned documents stored as image files in Amazon S3. The documents contain typewritten application forms with information including the applicant first name, applicant last name, application date, application type, and application text. The company has developed a machine learning algorithm to extract the metadata values from the scanned documents. The company wants to allow internal data analysts to analyze and find applications using the applicant name, application date, or application text.
The original images should also be downloadable. Cost control is secondary to query performance.
Which solution organizes the images and metadata to drive insights while meeting the requirements?

• A.For each image, use object tags to add the metadata. Use Amazon S3 Select to retrieve the files based on the applicant name and application date.
• B.Index the metadata and the Amazon S3 location of the image file in Amazon Elasticsearch Service.
  Allow the data analysts to use Kibana to submit queries to the Elasticsearch cluster.
• C.Store the metadata and the Amazon S3 location of the image file in an Amazon Redshift table. Allow the data analysts to run ad-hoc queries on the table.
• D.Store the metadata and the Amazon S3 location of the image files in an Apache Parquet file in Amazon S3, and define a table in the AWS Glue Data Catalog. Allow data analysts to use Amazon Athena to submit custom queries.

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A company's data analyst needs to ensure that queries executed in Amazon Athena cannot scan more than a prescribed amount of data for cost control purposes. Queries that exceed the prescribed threshold must be canceled immediately.
What should the data analyst do to achieve this?

• A.Configure Athena to invoke an AWS Lambda function that terminates queries when the prescribed threshold is crossed.
• B.For each workgroup, set the control limit for each query to the prescribed threshold.
• C.Enforce the prescribed threshold on all Amazon S3 bucket policies
• D.For each workgroup, set the workgroup-wide data usage control limit to the prescribed threshold.

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An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical data. The most recent 6 months of data will be queried frequently for analytics workloads. This data is several terabytes large. Once a month, historical data for the last 5 years must be accessible and will be joined with the more recent data. The company wants to optimize performance and cost.
Which storage solution will meet these requirements?

• A.Incrementally copy data from Amazon RDS to Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3. Use Amazon Athena to query the data.
• B.Incrementally copy data from Amazon RDS to Amazon S3. Load and store the most recent 6 months of data in Amazon Redshift. Configure an Amazon Redshift Spectrum table to connect to all historical data.
• C.Use an ETL tool to incrementally load the most recent 6 months of data into an Amazon Redshift cluster.
  Run more frequent queries against this cluster. Create a read replica of the RDS database to run queries on the historical data.
• D.Create a read replica of the RDS database to store the most recent 6 months of data. Copy the historical data into Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3 and Amazon RDS.
  Run historical queries using Amazon Athena.

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