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NEW QUESTION 49
A company is migrating its existing on-premises ETL jobs to Amazon EMR. The code consists of a series of jobs written in Java. The company needs to reduce overhead for the system administrators without changing the underlying code. Due to the sensitivity of the data, compliance requires that the company use root device volume encryption on all nodes in the cluster. Corporate standards require that environments be provisioned though AWS CloudFormation when possible.
Which solution satisfies these requirements?

A. Use a CloudFormation template to launch an EMR cluster. In the configuration section of the cluster, define a bootstrap action to encrypt the root device volume of every node.B. Install open-source Hadoop on Amazon EC2 instances with encrypted root device volumes. Configure the cluster in the CloudFormation template.C. Use a CloudFormation template to launch an EMR cluster. In the configuration section of the cluster, define a bootstrap action to enable TLS.D. Create a custom AMI with encrypted root device volumes. Configure Amazon EMR to use the custom AMI using the CustomAmild property in the CloudFormation template.

Answer: D

 

NEW QUESTION 50
A telecommunications company is looking for an anomaly-detection solution to identify fraudulent calls. The company currently uses Amazon Kinesis to stream voice call records in a JSON format from its on-premises database to Amazon S3. The existing dataset contains voice call records with 200 columns. To detect fraudulent calls, the solution would need to look at 5 of these columns only.
The company is interested in a cost-effective solution using AWS that requires minimal effort and experience in anomaly-detection algorithms.
Which solution meets these requirements?

A. Use Kinesis Data Firehose to detect anomalies on a data stream from Kinesis by running SQL queries, which compute an anomaly score for all calls and store the output in Amazon RDS. Use Amazon Athena to build a dataset and Amazon QuickSight to visualize the results.B. Use Kinesis Data Analytics to detect anomalies on a data stream from Kinesis by running SQL queries, which compute an anomaly score for all calls. Connect Amazon QuickSight to Kinesis Data Analytics to visualize the anomaly scores.C. Use an AWS Glue job to transform the data from JSON to Apache Parquet. Use AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Use Amazon Athena to create a table with a subset of columns. Use Amazon QuickSight to visualize the data and then use Amazon QuickSight machine learning-powered anomaly detection.D. Use an AWS Glue job to transform the data from JSON to Apache Parquet. Use AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Use Amazon SageMaker to build an anomaly detection model that can detect fraudulent calls by ingesting data from Amazon S3.

Answer: C

 

NEW QUESTION 51
An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical data. The most recent 6 months of data will be queried frequently for analytics workloads. This data is several terabytes large. Once a month, historical data for the last 5 years must be accessible and will be joined with the more recent data. The company wants to optimize performance and cost.
Which storage solution will meet these requirements?

A. Incrementally copy data from Amazon RDS to Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3. Use Amazon Athena to query the data.B. Incrementally copy data from Amazon RDS to Amazon S3. Load and store the most recent 6 months of data in Amazon Redshift. Configure an Amazon Redshift Spectrum table to connect to all historical data.C. Use an ETL tool to incrementally load the most recent 6 months of data into an Amazon Redshift cluster.
Run more frequent queries against this cluster. Create a read replica of the RDS database to run queries on the historical data.D. Create a read replica of the RDS database to store the most recent 6 months of data. Copy the historical data into Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3 and Amazon RDS.
Run historical queries using Amazon Athena.

Answer: B

 

NEW QUESTION 52
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