A data engineer is using AWS Glue ETL jobs to process data at frequent intervals The processed data is then copied into Amazon S3 The ETL jobs run every 15 minutes. The AWS Glue Data Catalog partitions need to be updated automatically after the completion of each job Which solution will meet these requirements MOST cost-effectively?

• A.Use the AWS Glue Data Catalog to manage the data catalog Use AWS Glue Studio to manage ETL jobs. Use the AWS Glue Studio feature that supports updates to the AWS Glue Data Catalog during job runs.
• B.Use the AWS Glue Data Catalog to manage the data catalog Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.
• C.Use the AWS Glue Data Catalog to manage the data catalog Define an AWS Glue workflow for the ETL process Define a trigger within the workflow that can start the crawler when an ETL job run is complete
• D.Use an Apache Hive metastore to manage the data catalog Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.

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A mobile gaming company wants to capture data from its gaming app and make the data available for analysis immediately. The data record size will be approximately 20 KB. The company is concerned about achieving optimal throughput from each device. Additionally, the company wants to develop a data stream processing application with dedicated throughput for each consumer.
Which solution would achieve this goal?

• A.Have the app call the PutRecords API to send data to Amazon Kinesis Data Streams. Use the enhanced fan-out feature while consuming the data.
• B.Have the app call the PutRecordBatch API to send data to Amazon Kinesis Data Firehose. Submit a support case to enable dedicated throughput on the account.
• C.Have the app use Amazon Kinesis Producer Library (KPL) to send data to Kinesis Data Firehose. Use the enhanced fan-out feature while consuming the data.
• D.Have the app call the PutRecords API to send data to Amazon Kinesis Data Streams. Host the stream- processing application on Amazon EC2 with Auto Scaling.

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A company has 1 million scanned documents stored as image files in Amazon S3. The documents contain typewritten application forms with information including the applicant first name, applicant last name, application date, application type, and application text. The company has developed a machine learning algorithm to extract the metadata values from the scanned documents. The company wants to allow internal data analysts to analyze and find applications using the applicant name, application date, or application text.
The original images should also be downloadable. Cost control is secondary to query performance.
Which solution organizes the images and metadata to drive insights while meeting the requirements?

• A.For each image, use object tags to add the metadata. Use Amazon S3 Select to retrieve the files based on the applicant name and application date.
• B.Index the metadata and the Amazon S3 location of the image file in Amazon Elasticsearch Service.
  Allow the data analysts to use Kibana to submit queries to the Elasticsearch cluster.
• C.Store the metadata and the Amazon S3 location of the image file in an Amazon Redshift table. Allow the data analysts to run ad-hoc queries on the table.
• D.Store the metadata and the Amazon S3 location of the image files in an Apache Parquet file in Amazon S3, and define a table in the AWS Glue Data Catalog. Allow data analysts to use Amazon Athena to submit custom queries.

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A company is migrating its existing on-premises ETL jobs to Amazon EMR. The code consists of a series of jobs written in Jav a. 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.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.
• 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.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.

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A media analytics company consumes a stream of social media posts. The posts are sent to an Amazon Kinesis data stream partitioned on user_id. An AWS Lambda function retrieves the records and validates the content before loading the posts into an Amazon Elasticsearch cluster. The validation process needs to receive the posts for a given user in the order they were received. A data analyst has noticed that, during peak hours, the social media platform posts take more than an hour to appear in the Elasticsearch cluster.
What should the data analyst do reduce this latency?

• A.Migrate the validation process to Amazon Kinesis Data Firehose.
• B.Increase the number of shards in the stream.
• C.Configure multiple Lambda functions to process the stream.
• D.Migrate the Lambda consumers from standard data stream iterators to an HTTP/2 stream consumer.

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