A company uses Amazon Redshift as its data warehouse. A new table has columns that contain sensitive dat a. The data in the table will eventually be referenced by several existing queries that run many times a day.
A data analyst needs to load 100 billion rows of data into the new table. Before doing so, the data analyst must ensure that only members of the auditing group can read the columns containing sensitive data.
How can the data analyst meet these requirements with the lowest maintenance overhead?

• A.Load all the data into the new table and grant the auditing group permission to read from the table. Load all the data except for the columns containing sensitive data into a second table. Grant the appropriate users read-only permissions to the second table.
• B.Load all the data into the new table and grant the auditing group permission to read from the table. Use the GRANT SQL command to allow read-only access to a subset of columns to the appropriate users.
• C.Load all the data into the new table and grant all users read-only permissions to non-sensitive columns. Attach an IAM policy to the auditing group with explicit ALLOW access to the sensitive data columns.
• D.Load all the data into the new table and grant the auditing group permission to read from the table. Create a view of the new table that contains all the columns, except for those considered sensitive, and grant the appropriate users read-only permissions to the table.

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A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:
* Daily transformations of 300 GB of data with different file formats landing in Amazon S3 at a scheduled time.
* One-time transformations of terabytes of archived data residing in the S3 data lake.
Which combination of solutions cost-effectively meets the company's requirements for transforming the data?
(Choose three.)

• A.For archived data, use Amazon EMR to perform data transformations.
• B.For archived data, use Amazon SageMaker to perform data transformations.
• C.For daily incoming data, use Amazon Athena to scan and identify the schema.
• D.For daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform transformations.
• E.For daily incoming data, use Amazon Redshift to perform transformations.
• F.For daily incoming data, use AWS Glue crawlers to scan and identify the schema.

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A company stores Apache Parquet-formatted files in Amazon S3 The company uses an AWS Glue Data Catalog to store the table metadata and Amazon Athena to query and analyze the data The tables have a large number of partitions The queries are only run on small subsets of data in the table A data analyst adds new time partitions into the table as new data arrives The data analyst has been asked to reduce the query runtime Which solution will provide the MOST reduction in the query runtime?

• A.Add more partitions to be used over the table. Then filter over two partitions and put all columns in the WHERE clause
• B.Convert the Parquet files to the Apache ORC file format. Then attempt to query the data again
• C.Use partition projection to speed up the processing of the partitioned table
• D.Convert the Parquet files to the csv file format..Then attempt to query the data again

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A data analyst is using Amazon QuickSight for data visualization across multiple datasets generated by applications. Each application stores files within a separate Amazon S3 bucket. AWS Glue Data Catalog is used as a central catalog across all application data in Amazon S3. A new application stores its data within a separate S3 bucket. After updating the catalog to include the new application data source, the data analyst created a new Amazon QuickSight data source from an Amazon Athena table, but the import into SPICE failed.
How should the data analyst resolve the issue?

• A.Edit the permissions for the AWS Glue Data Catalog from within the AWS Glue console.
• B.Edit the permissions for the AWS Glue Data Catalog from within the Amazon QuickSight console.
• C.Edit the permissions for the new S3 bucket from within the Amazon QuickSight console.
• D.Edit the permissions for the new S3 bucket from within the S3 console.

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A mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the data to DynamoDB. The finance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive fields. The Amazon Redshift cluster is shared with other data analysts from different business units.
Which steps should a data analyst take to accomplish this task efficiently and securely?

• A.Create an Amazon EMR cluster with an EMR_EC2_DefaultRole role that has access to the KMS key.
  Create Apache Hive tables that reference the data stored in DynamoDB and the finance table in Amazon Redshift. In Hive, select the data from DynamoDB and then insert the output to the finance table in Amazon Redshift.
• B.Create an AWS Lambda function to process the DynamoDB stream. Decrypt the sensitive data using the same KMS key. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command to load the data from Amazon S3 to the finance table.
• C.Create an AWS Lambda function to process the DynamoDB stream. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command with the IAM role that has access to the KMS key to load the data from S3 to the finance table.
• D.Create an Amazon EMR cluster. Create Apache Hive tables that reference the data stored in DynamoDB. Insert the output to the restricted Amazon S3 bucket for the finance team. Use the COPY command with the IAM role that has access to the KMS key to load the data from Amazon S3 to the finance table in Amazon Redshift.

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