A company has customer data in CSV format. The company stores the data in Amazon S3 and catalogs the data in an AWS Glue Data Catalog. The company has an Amazon Redshift cluster that contains historical call center data. The
cluster has a heavy load, and the company does not want to load any new data into the cluster.
Data analysts want to JOIN the customer data that is in Amazon S3 with the historical call center data that is in Amazon Redshift. The data analysts will use a daily batch process that takes multiple hours to run.
Which solution will meet these requirements with the LEAST operational overhead?
A. Unload the historical call center data from Amazon Redshift to Amazon S3 by using an AWS Lambda function. Perform the JOIN with the customer data that resides in Amazon S3 by using AWS Glue ETL scripts.
B. Export the historical call center data from Amazon Redshift to an Amazon EC2 instance by using the AWS CLI. Perform the JOIN with the customer data that resides in Amazon S3 by using AWS Glue ETL scripts.
C. Create an external table by using Amazon Redshift Spectrum for the customer data that resides in Amazon S3. Perform the JOIN with the historical call center data by using Amazon Redshift.
D. Export the historical call center data from Amazon Redshift to an Amazon EMR cluster by using Apache Sqoop. Perform the JOIN with the customer data that resides in Amazon S3 by using Apache Hive.
A company uses Amazon Redshift to store historical sales transactions. The company must encrypt data at rest in the Redshift cluster. The company also must store encryption keys by using an on-premises hardware security module (HSM). Which solution will meet these requirements with the LEAST operational overhead?
A. Create and store encryption keys by using AWS CloudHSM Classic. Launch a new Redshift cluster with the option to use CloudHSM Classic to store keys.
B. Establish an AWS Site-to-Site VPN connection between the existing VPC and the on-premises network. Create an HSM connection and a client certificate for the on-premises HSM. Launch a new Redshift cluster in the VPC with the option to use the on-premises HSM to store keys.
C. Establish an AWS Site-to-Site VPN connection between the existing VPC and the on-premises network. Create an HSM connection and a client certificate for the on-premises HSM. Configure the existing Redshift cluster in the VPC with the option to use the on-premises HSM to store keys. Reboot the Redshift cluster.
D. Create a replica of the on-premises HSM in AWS CloudHSM. Launch a new Redshift cluster with the option to use CloudHSM to store keys.
A company needs to implement a solution to restrict the launch of new Amazon EMR clusters in public subnets. With the exception of SSH and HTTPS connections, no employee should be able to launch a new EMR cluster in a public subnet unless inbound traffic from the internet is blocked.
Which combination of steps should the company take to meet this requirement? (Choose two.)
A. Turn on EMR block public access for an IAM user group. Add all the employees to the group.
B. Turn on EMR block public access for the account.
C. Add port 443 as an exception in the block public access configuration.
D. Add port 22 as an exception in the block public access configuration.
E. Create a private internal subnet. Require all the employees to specify this subnet when they launch clusters.
A large fashion retailer wants to transform a source dataset to a consumable format. The retailer is building an ETL pipeline and needs to deduplicate the data because the retailer's various departments share similar customer and stock information. The retailer wants to build a data lake in Amazon S3 after the transformation and deduplication processes are completed.
Which solution MOST cost-effectively meets these requirements?
A. Load the data into Amazon Redshift and build custom deduplication scripts by using SQL. Use the UNLOAD command in Amazon Redshift to store the data in Amazon S3.
B. Use AWS Glue to transform the data and use FindMatches to deduplicate the data. Store the output in Amazon S3.
C. Use Amazon EMR to transform the data. Deduplicate the data by using custom Spark SQL scripts and use EMRFS to store the output in Amazon S3.
D. Use an Amazon Athena federated query to load the data from the sources. Build custom Athena SQL scripts to deduplicate and store the output to Amazon S3.
A workforce management company has built business intelligence dashboards in Amazon QuickSight Enterprise edition to help the company understand staffing behavior for its customers. The company stores data for these dashboards in Amazon S3 and performs queries by using Amazon Athena. The company has millions of records from many years of data.
A data analytics specialist observes sudden changes in overall staffing revenue in one of the dashboards. The company thinks that these sudden changes are because of outliers in data for some customers. The data analytics specialist must implement a solution to explain and identify the primary reasons for these changes.
Which solution will meet these requirements with the LEAST development effort?
A. Add a box plot visual type in QuickSight to compare staffing revenue by customer.
B. Use the anomaly detection and contribution analysis feature in QuickSight.
C. Create a custom SQL script in Athena. Invoke an anomaly detection machine learning model.
D. Use S3 analytics storage class analysis to detect anomalies for data written to Amazon S3.
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 is designing a support ticketing system for its employees. The company has a flattened LDAP dataset that contains employee data. The data includes ticket categories that the employees can access, relevant ticket metadata stored in Amazon S3, and the business unit of each employee.
The company uses Amazon QuickSight to visualize the data. The company needs an automated solution to apply row-level data restriction within the QuickSight group for each business unit. The solution must grant access to an employee when an employee is added to a business unit and must deny access to an employee when an employee is removed from a business unit.
Which solution will meet these requirements?
A. Load the dataset into SPICE from Amazon S3. Create a SPICE query that contains the dataset rules for row-level security. Upload separate .csv files to Amazon S3 for adding and removing users from a group. Apply the permissions dataset on the existing QuickSight users. Create an AWS Lambda function that will run periodically to refresh the direct query cache based on the changes to the .csv file.
B. Load the dataset into SPICE from Amazon S3. Create an AWS Lambda function that will run each time the direct query cache is refreshed. Configure the Lambda function to apply a permissions file to the dataset that is loaded into SPICE. Configure the addition and removal of groups and users by creating a QuickSight IAM policy.
C. Load the dataset into SPICE from Amazon S3. Apply a permissions file to the dataset to dictate which group has access to the dataset. Upload separate .csv files to Amazon S3 for adding and removing groups and users under the path that QuickSight is reading from. Create an AWS Lambda function that will run when a particular object is uploaded to Amazon S3. Configure the Lambda function to make API calls to QuickSight to add or remove users or a group.
D. Move the data from Amazon S3 into Amazon Redshift. Load the dataset into SPICE from Amazon Redshift. Create an AWS Lambda function that will run each time the direct query cache is refreshed. Configure the Lambda function to apply a permissions file to the dataset that is loaded into SPICE.
A company is creating a data lake by using AWS Lake Formation. The data that will be stored in the data lake contains sensitive customer information and must be encrypted at rest using an AWS Key Management Service (AWS KMS) customer managed key to meet regulatory requirements.
How can the company store the data in the data lake to meet these requirements?
A. Store the data in an encrypted Amazon Elastic Block Store (Amazon EBS) volume. Register the Amazon EBS volume with Lake Formation.
B. Store the data in an Amazon S3 bucket by using server-side encryption with AWS KMS (SSE-KMS). Register the S3 location with Lake Formation.
C. Encrypt the data on the client side and store the encrypted data in an Amazon S3 bucket. Register the S3 location with Lake Formation.
D. Store the data in an Amazon S3 Glacier Flexible Retrieval vault bucket. Register the S3 Glacier Flexible Retrieval vault with Lake Formation.
A financial company recently added more features to its mobile app. This required the creation of a new topic named mobile_transfers in the existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. A few days after adding this new topic, an Amazon CloudWatch alarm for the RootDiskUsed metric for the MSK cluster was raised.
How should a data specialist resolve this issue?
A. Expand the storage of the MSK broker. Configure storage auto-expansion.
B. Increase the storage for the Apache ZooKeeper nodes.
C. Update the MSK broker instance to the next larger type. Restart the MSK cluster.
D. Specify the Target-Volume-in-GiB parameter for the mobile_transfers topic.
A company hosts its analytics solution on premises. The analytics solution includes a server that collects log files. The analytics solution uses an Apache Hadoop cluster to analyze the log files hourly and to produce output files. All the files are archived to another server for a specified duration.
The company is expanding globally and plans to move the analytics solution to multiple AWS Regions in the AWS Cloud. The company must adhere to the data archival and retention requirements of each country where the data is stored.
Which solution will meet these requirements?
A. Create an Amazon S3 bucket in one Region to collect the log files. Use S3 event notifications to invoke an AWS Glue job for log analysis. Store the output files in the target S3 bucket. Use S3 Lifecycle rules on the target S3 bucket to set an expiration period that meets the retention requirements of the country that contains the Region.
B. Create a Hadoop Distributed File System (HDFS) file system on an Amazon EMR cluster in one Region to collect the log files. Set up a bootstrap action on the EMR cluster to run an Apache Spark job. Store the output files in a target Amazon S3 bucket. Schedule a job on one of the EMR nodes to delete files that no longer need to be retained.
C. Create an Amazon S3 bucket in each Region to collect log files. Create an Amazon EMR cluster. Submit steps on the EMR cluster for analysis. Store the output files in a target S3 bucket in each Region. Use S3 Lifecycle rules on each target S3 bucket to set an expiration period that meets the retention requirements of the country that contains the Region.
D. Create an Amazon Kinesis Data Firehose delivery stream in each Region to collect log data. Specify an Amazon S3 bucket in each Region as the destination. Use S3 Storage Lens for data analysis. Use S3 Lifecycle rules on each destination S3 bucket to set an expiration period that meets the retention requirements of the country that contains the Region.
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