A company uses the Amazon Kinesis SDK to write data to Kinesis Data Streams. Compliance requirements state that the data must be encrypted at rest using a key that can be rotated. The company wants to meet this encryption requirement with minimal coding effort.

How can these requirements be met?

A. Create a customer master key (CMK) in AWS KMS. Assign the CMK an alias. Use the AWS Encryption SDK, providing it with the key alias to encrypt and decrypt the data.

B. Create a customer master key (CMK) in AWS KMS. Assign the CMK an alias. Enable server-side encryption on the Kinesis data stream using the CMK alias as the KMS master key.

C. Create a customer master key (CMK) in AWS KMS. Create an AWS Lambda function to encrypt and decrypt the data. Set the KMS key ID in the function's environment variables.

D. Enable server-side encryption on the Kinesis data stream using the default KMS key for Kinesis Data Streams.

A company has a marketing department and a finance department. The departments are storing data in Amazon S3 in their own AWS accounts in AWS Organizations. Both departments use AWS Lake Formation to catalog and secure their

data. The departments have some databases and tables that share common names.

The marketing department needs to securely access some tables from the finance department.

Which two steps are required for this process? (Choose two.)

A. The finance department grants Lake Formation permissions for the tables to the external account for the marketing department.

B. The finance department creates cross-account IAM permissions to the table for the marketing department role.

C. The marketing department creates an IAM role that has permissions to the Lake Formation tables.

A company needs to collect streaming data from several sources and store the data in the AWS Cloud. The dataset is heavily structured, but analysts need to perform several complex SQL queries and need consistent performance. Some of the data is queried more frequently than the rest. The company wants a solution that meets its performance requirements in a cost-effective manner.

Which solution meets these requirements?

A. Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon S3. Use Amazon Athena to perform SQL queries over the ingested data.

B. Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads.

C. Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads.

D. Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon S3. Load frequently queried data to Amazon Redshift using the COPY command. Use Amazon Redshift Spectrum for less frequently queried data.

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. Place the data sources for Australia and the US into separate SPICE capacity pools.

B. Set up an Amazon Redshift VPC security group for Australia and the US.

C. Deploy QuickSight Enterprise edition to implement row-level security (RLS) to the sales table.

D. Deploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.

A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to run. The company needs to optimize query performance and avoid any downtime.

What is the MOST cost-effective solution?

A. Turn on concurrency scaling in the workload management (WLM) queue.

B. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.

C. Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.

D. Use a snapshot, restore, and resize operation. Switch to the new target cluster.

A company's marketing and finance departments are storing data in Amazon S3 in their respective AWS accounts managed by AWS Organizations. Both departments use AWS Lake Formation to catalog and secure their data in Amazon S3. The finance department needs to share some tables with the marketing department for reporting purposes.

Which steps are required to complete this process? (Choose two.)

A. The finance department grants Lake Formation permissions for the shared tables to the marketing department's AWS account.

B. The finance department creates a cross-account IAM role with permission to access the shared tables.

C. Users from the marketing department account assume a cross-account IAM role in the finance department account that has permission to access the shared tables.

D. The marketing department creates a resource link to access the shared tables from the finance department.

E. The finance department creates and shares a resource link with the marketing department to access the shared tables.

A company wants to use automatic machine learning (ML) to create and visualize forecasts of complex scenarios and trends. Which solution will meet these requirements with the LEAST management overhead?

A. Use an AWS Glue ML job to transform the data and create forecasts. Use Amazon QuickSight to visualize the data.

B. Use Amazon QuickSight to visualize the data. Use ML-powered forecasting in QuickSight to create forecasts.

C. Use a prebuilt ML AMI from the AWS Marketplace to create forecasts. Use Amazon QuickSight to visualize the data.

D. Use Amazon SageMaker inference pipelines to create and update forecasts. Use Amazon QuickSight to visualize the combined data.

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 QuickSight to modify the current dataset to use SPICE

B. Use AWS Glue to create an Apache Spark job that joins the fact table with the dimensions. Load the data into a new table

C. Use Amazon Redshift to create a materialized view that joins the fact table with the dimensions D. Use Amazon Redshift to create a stored procedure that joins the fact table with the dimensions Load the data into a new table

A company needs to launch an Amazon EMR cluster in a VPC. The EMR cluster must not have any access to the internet. Additionally, the EMR cluster's access to other AWS services must not be through the internet. Which solution will meet these requirements?

A. Launch the EMR cluster in a private subnet. Configure a NAT gateway for access to other AWS services.

B. Launch the EMR cluster in a private subnet. Configure a NAT instance for access to other AWS services.

C. Launch the EMR cluster in a private subnet. Configure a VPC endpoint for access to other AWS services.

D. Launch the EMR cluster in a public subnet. Configure a VPC endpoint for access to other AWS services.

A company uses Amazon Redshift for its data warehouse. The company is running an ETL process that receives data in data parts from five third-party providers. The data parts contain independent records that are related to one specific job.

The company receives the data parts at various times throughout each day.

A data analytics specialist must implement a solution that loads the data into Amazon Redshift only after the company receives all five data parts.

Which solution will meet these requirements?

A. Create an Amazon S3 bucket to receive the data. Use S3 multipart upload to collect the data from the different sources and to form a single object before loading the data into Amazon Redshift.

B. Use an AWS Lambda function that is scheduled by cron to load the data into a temporary table in Amazon Redshift. Use Amazon Redshift database triggers to consolidate the final data when all five data parts are ready.

C. Create an Amazon S3 bucket to receive the data. Create an AWS Lambda function that is invoked by S3 upload events. Configure the function to validate that all five data parts are gathered before the function loads the data into Amazon Redshift.

D. Create an Amazon Kinesis Data Firehose delivery stream. Program a Python condition that will invoke a buffer flush when all five data parts are received.