A system needs to collect on-premises application spool files into a persistent storage layer in AWS. Each spool file is 2 KB. The application generates 1 M files per hour. Each source file is automatically deleted from the local server after an hour.
What is the most cost-efficient option to meet these requirements?
A. Write file contents to an Amazon DynamoDB table.
B. Copy files to Amazon S3 Standard Storage.
C. Write file contents to Amazon ElastiCache.
D. Copy files to Amazon S3 infrequent Access Storage.
A telecommunications company needs to predict customer churn (i.e., customers who decide to switch to a competitor). The company has historic records of each customer, including monthly consumption patterns, calls to customer service, and whether the customer ultimately quit the service. All of this data is stored in Amazon S3. The company needs to know which customers are likely going to churn soon so that they can win back their loyalty.
What is the optimal approach to meet these requirements?
A. Use the Amazon Machine Learning service to build the binary classification model based on the dataset stored in Amazon S3. The model will be used regularly to predict churn attribute for existing customers.
B. Use AWS QuickSight to connect it to data stored in Amazon S3 to obtain the necessary business insight. Plot the churn trend graph to extrapolate churn likelihood for existing customers.
C. Use EMR to run the Hive queries to build a profile of a churning customer. Apply a profile to existing customers to determine the likelihood of churn.
D. Use a Redshift cluster to COPY the data from Amazon S3. Create a User Defined Function in Redshift that computes the likelihood of churn.
The department of transportation for a major metropolitan area has placed sensors on roads at key locations around the city. The goal is to analyze the flow of traffic and notifications from emergency services to identify potential issues and to help planners correct trouble spots.
A data engineer needs a scalable and fault-tolerant solution that allows planners to respond to issues within 30 seconds of their occurrence.
Which solution should the data engineer choose?
A. Collect the sensor data with Amazon Kinesis Firehose and store it in Amazon Redshift for analysis. Collect emergency services events with Amazon SQS and store in Amazon DynampDB for analysis.
B. Collect the sensor data with Amazon SQS and store in Amazon DynamoDB for analysis. Collect emergency services events with Amazon Kinesis Firehose and store in Amazon Redshift for analysis.
C. Collect both sensor data and emergency services events with Amazon Kinesis Streams and use DynamoDB for analysis.
D. Collect both sensor data and emergency services events with Amazon Kinesis Firehose and use Amazon Redshift for analysis.
An organization uses Amazon Elastic MapReduce(EMR) to process a series of extract-transform-load (ETL) steps that run in sequence. The output of each step must be fully processed in subsequent steps but will not be retained.
Which of the following techniques will meet this requirement most efficiently?
A. Use the EMR File System (EMRFS) to store the outputs from each step as objects in Amazon Simple Storage Service (S3).
B. Use the s3n URI to store the data to be processed as objects in Amazon S3.
C. Define the ETL steps as separate AWS Data Pipeline activities.
D. Load the data to be processed into HDFS, and then write the final output to Amazon S3.
An administrator is processing events in near real-time using Kinesis streams and Lambda. Lambda intermittently fails to process batches from one of the shards due to a 5-munite time limit.
What is a possible solution for this problem?
A. Add more Lambda functions to improve concurrent batch processing.
B. Reduce the batch size that Lambda is reading from the stream.
C. Ignore and skip events that are older than 5 minutes and put them to Dead Letter Queue (DLQ).
D. Configure Lambda to read from fewer shards in parallel.
An organization uses a custom map reduce application to build monthly reports based on many small data files in an Amazon S3 bucket. The data is submitted from various business units on a frequent but unpredictable schedule. As the dataset continues to grow, it becomes increasingly difficult to process all of the data in one day. The organization has scaled up its Amazon EMR cluster, but other optimizations could improve performance.
The organization needs to improve performance with minimal changes to existing processes and applications.
What action should the organization take?
A. Use Amazon S3 Event Notifications and AWS Lambda to create a quick search file index in DynamoDB.
B. Add Spark to the Amazon EMR cluster and utilize Resilient Distributed Datasets in-memory.
C. Use Amazon S3 Event Notifications and AWS Lambda to index each file into an Amazon Elasticsearch Service cluster.
D. Schedule a daily AWS Data Pipeline process that aggregates content into larger files using S3DistCp.
E. Have business units submit data via Amazon Kinesis Firehose to aggregate data hourly into Amazon S3.
A company is building a new application in AWS. The architect needs to design a system to collect application log events. The design should be a repeatable pattern that minimizes data loss if an application instance fails, and keeps a durable copy of a log data for at least 30 days.
What is the simplest architecture that will allow the architect to analyze the logs?
A. Write them directly to a Kinesis Firehose. Configure Kinesis Firehose to load the events into an Amazon Redshift cluster for analysis.
B. Write them to a file on Amazon Simple Storage Service (S3). Write an AWS Lambda function that runs in response to the S3 event to load the events into Amazon Elasticsearch Service for analysis.
C. Write them to the local disk and configure the Amazon CloudWatch Logs agent to load the data into CloudWatch Logs and subsequently into Amazon Elasticsearch Service.
D. Write them to CloudWatch Logs and use an AWS Lambda function to load them into HDFS on an Amazon Elastic MapReduce (EMR) cluster for analysis.
Managers in a company need access to the human resources database that runs on Amazon Redshift, to run reports about their employees. Managers must only see information about their direct reports.
Which technique should be used to address this requirement with Amazon Redshift?
A. Define an IAM group for each manager with each employee as an IAM user in that group, and use that to limit the access.
B. Use Amazon Redshift snapshot to create one cluster per manager. Allow the managers to access only their designated clusters.
C. Define a key for each manager in AWS KMS and encrypt the data for their employees with their private keys.
D. Define a view that uses the employee's manager name to filter the records based on current user names.
An Amazon Redshift Database is encrypted using KMS. A data engineer needs to use the AWS CLI to create a KMS encrypted snapshot of the database in another AWS region.
Which three steps should the data engineer take to accomplish this task? (Choose three.)
A. Create a new KMS key in the destination region.
B. Copy the existing KMS key to the destination region.
C. Use CreateSnapshotCopyGrant to allow Amazon Redshift to use the KMS key from the source region.
D. In the source region, enable cross-region replication and specify the name of the copy grant created.
E. In the destination region, enable cross-region replication and specify the name of the copy grant created.
F. Use CreateSnapshotCopyGrant to allow Amazon Redshift to use the KMS key created in the destination region.
A customer has a machine learning workflow that consists of multiple quick cycles of reads-writes-reads on Amazon S3. The customer needs to run the workflow on EMR but is concerned that the reads in subsequent cycles will miss new data critical to the machine learning from the prior cycles.
How should the customer accomplish this?
A. Turn on EMRFS consistent view when configuring the EMR cluster.
B. Use AWS Data Pipeline to orchestrate the data processing cycles.
C. Set hadoop.data.consistency = true in the core-site.xml file.
D. Set hadoop.s3.consistency = true in the core-site.xml file.
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