A data architect is building an Amazon S3 data lake for a bank. The goal is to provide a single data repository for customer data needs, such as personalized recommendations. The bank uses Amazon Kinesis Data Firehose to ingest customers' personal information bank accounts, and transactions in near-real time from a transactional relational database. The bank requires all personally identifiable information (PII) that is stored in the AWS Cloud to be masked.
Which solution will meet these requirements?
A. Invoke an AWS Lambda function from Kinesis Data Firehose to mask PII before delivering the data into Amazon S3.
B. Use Amazon Made, and configure it to discover and mask PII.
C. Enable server-side encryption (SSE) in Amazon S3.
D. Invoke Amazon Comprehend from Kinesis Data Firehose to detect and mask PII before delivering the data into Amazon S3.
An analytics software as a service (SaaS) provider wants to offer its customers business intelligence (BI) reporting capabilities that are self-service. The provider is using Amazon QuickSight to build these reports. The data for the reports resides in a multi-tenant database, but each customer should only be able to access their own data.
The provider wants to give customers two user role options:
Read-only users for individuals who only need to view dashboards.
Power users for individuals who are allowed to create and share new dashboards with other users.
Which QuickSught feature allows the provider to meet these requirements?
A. Embedded dashboards
B. Table calculations
C. Isolated namespaces
D. SPICE
A company recently created a test AWS account to use for a development environment. The company also created a production AWS account in another AWS Region. As part of its security testing, the company wants to send log data from Amazon CloudWatch Logs in its production account to an Amazon Kinesis data stream in its test account.
Which solution will allow the company to accomplish this goal?
A. Create a subscription filter in the production account's CloudWatch Logs to target the Kinesis data stream in the test account as its destination. In the test account, create an IAM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account.
B. In the test account, create an IAM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account. Create a destination data stream in Kinesis Data Streams in the test account with an IAM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account.
C. In the test account, create an IAM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account. Create a destination data stream in Kinesis Data Streams in the test account with an IAM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account.
D. Create a destination data stream in Kinesis Data Streams in the test account with an IAM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account. Create a subscription filter in the production account's CloudWatch Logs to target the Kinesis data stream in the test account as its destination.
A company is Running Apache Spark on an Amazon EMR cluster. The Spark job writes to an Amazon S3 bucket. The job fails and returns an HTTP 503 "Slow Down" AmazonS3Exception error.
Which actions will resolve this error? (Choose two.)
A. Add additional prefixes to the S3 bucket
B. Reduce the number of prefixes in the S3 bucket
C. Increase the EMR File System (EMRFS) retry limit
D. Disable dynamic partition pruning in the Spark configuration for the cluster
E. Add more partitions in the Spark configuration for the cluster
A hospital is building a research data lake to ingest data from electronic health records (EHR) systems from multiple hospitals and clinics. The EHR systems are independent of each other and do not have a common patient identifier. The data engineering team is not experienced in machine learning (ML) and has been asked to generate a unique patient identifier for the ingested records.
Which solution will accomplish this task?
A. An AWS Glue ETL job with the FindMatches transform
B. Amazon Kendra
C. Amazon SageMaker Ground Truth
D. An AWS Glue ETL job with the ResolveChoice transform
An online retail company uses Amazon Redshift to store historical sales transactions. The company is required to encrypt data at rest in the clusters to comply with the Payment Card Industry Data Security Standard (PCI DSS). A corporate governance policy mandates management of encryption keys using an on-premises hardware security module (HSM).
Which solution meets these requirements?
A. Create and manage encryption keys using AWS CloudHSM Classic. Launch an Amazon Redshift cluster in a VPC with the option to use CloudHSM Classic for key management.
B. Create a VPC and establish a VPN connection between the VPC and the on-premises network. Create an HSM connection and client certificate for the on-premises HSM. Launch a cluster in the VPC with the option to use the on-premises HSM to store keys.
C. Create an HSM connection and client certificate for the on-premises HSM. Enable HSM encryption on the existing unencrypted cluster by modifying the cluster. Connect to the VPC where the Amazon Redshift cluster resides from the on-premises network using a VPN.
D. Create a replica of the on-premises HSM in AWS CloudHSM. Launch a cluster in a VPC with the option to use CloudHSM to store keys.
A company operates toll services for highways across the country and collects data that is used to understand usage patterns. Analysts have requested the ability to run traffic reports in near-real time. The company is interested in building an ingestion pipeline that loads all the data into an Amazon Redshift cluster and alerts operations personnel when toll traffic for a particular toll station does not meet a specified threshold. Station data and the corresponding threshold values are stored in Amazon S3.
Which approach is the MOST efficient way to meet these requirements?
A. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneously. Create a reference data source in Kinesis Data Analytics to temporarily store the threshold values from Amazon S3 and compare the count of vehicles for a particular toll station against its corresponding threshold value. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.
B. Use Amazon Kinesis Data Streams to collect all the data from toll stations. Create a stream in Kinesis Data Streams to temporarily store the threshold values from Amazon S3. Send both streams to Amazon Kinesis Data Analytics to compare the count of vehicles for a particular toll station against its corresponding threshold value. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met. Connect Amazon Kinesis Data Firehose to Kinesis Data Streams to deliver the data to Amazon Redshift.
C. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift. Then, automatically trigger an AWS Lambda function that queries the data in Amazon Redshift, compares the count of vehicles for a particular toll station against its corresponding threshold values read from Amazon S3, and publishes an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.
D. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneously. Use Kinesis Data Analytics to compare the count of vehicles against the threshold value for the station stored in a table as an in-application stream based on information stored in Amazon S3. Configure an AWS Lambda function as an output for the application that will publish an Amazon Simple Queue Service (Amazon SQS) notification to alert operations personnel if the threshold is not met.
A company is hosting an enterprise reporting solution with Amazon Redshift. The application provides reporting capabilities to three main groups: an executive group to access financial reports, a data analyst group to run long-running ad-hoc queries, and a data engineering group to run stored procedures and ETL processes. The executive team requires queries to run with optimal performance. The data engineering team expects queries to take minutes.
Which Amazon Redshift feature meets the requirements for this task?
A. Concurrency scaling
B. Short query acceleration (SQA)
C. Workload management (WLM) D. Materialized views
A global pharmaceutical company receives test results for new drugs from various testing facilities worldwide. The results are sent in millions of 1 KB-sized JSON objects to an Amazon S3 bucket owned by the company. The data engineering team needs to process those files, convert them into Apache Parquet format, and load them into Amazon Redshift for data analysts to perform dashboard reporting. The engineering team uses AWS Glue to process the objects, AWS Step Functions for process orchestration, and Amazon CloudWatch for job scheduling.
More testing facilities were recently added, and the time to process files is increasing.
What will MOST efficiently decrease the data processing time?
A. Use AWS Lambda to group the small files into larger files. Write the files back to Amazon S3. Process the files using AWS Glue and load them into Amazon Redshift tables.
B. Use the AWS Glue dynamic frame file grouping option while ingesting the raw input files. Process the files and load them into Amazon Redshift tables.
C. Use the Amazon Redshift COPY command to move the files from Amazon S3 into Amazon Redshift tables directly. Process the files in Amazon Redshift.
D. Use Amazon EMR instead of AWS Glue to group the small input files. Process the files in Amazon EMR and load them into Amazon Redshift tables.
An education provider's learning management system (LMS) is hosted in a 100 TB data lake that is built on Amazon S3. The provider's LMS supports hundreds of schools. The provider wants to build an advanced analytics reporting platform using Amazon Redshift to handle complex queries with optimal performance. System users will query the most recent 4 months of data 95% of the time while 5% of the queries will leverage data from the previous 12 months.
Which solution meets these requirements in the MOST cost-effective way?
A. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Use S3 lifecycle management rules to store data from the previous 12 months in Amazon S3 Glacier storage.
B. Leverage DS2 nodes for the Amazon Redshift cluster. Migrate all data from Amazon S3 to Amazon Redshift. Decommission the data lake.
C. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Ensure the S3 Standard storage class is in use with objects in the data lake.
D. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift federated queries to join cluster data with the data lake to reduce costs. Ensure the S3 Standard storage class is in use with objects in the data lake.
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