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AWS-Certified-Big-Data-Specialty-BDS-C00 – AWS Certified Big Data - Specialty (BDS-C00)

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Showing 10–12 of 15 questions

Question 10

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 minimal changes to existing processes and applications. What action should the organization take?

Select an option, then click Submit answer.

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    Use Amazon S3 Event Notifications and AWS Lambda to create a quick search file index in DynamoDB.
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    Add Spark to the Amazon EMR cluster and utilize Resilient Distributed Datasets in-memory.
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    Use Amazon S3 Event Notifications and AWS Lambda to index each file into an Amazon Elasticsearch Service cluster.
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    Schedule a daily AWS Data Pipeline process that aggregates content into larger files using S3DistCp.
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    Have business units submit data via Amazon Kinesis Firehose to aggregate data hourly into Amazon S3.
Question 11

An organization is developing a mobile social application and needs to collect logs from all devices on which it is installed. The organization is evaluating the Amazon Kinesis Data Streams to push logs and Amazon EMR to process data. They want to store data on HDFS using the default replication factor to replicate data among the cluster, but they are concerned about the durability of the data. Currently, they are producing 300

GB of raw data daily, with additional spikes during special events. They will need to scale out the Amazon EMR cluster to match the increase in streamed data.

Which solution prevents data loss and matches compute demand?

Select an option, then click Submit answer.

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    Use multiple Amazon EBS volumes on Amazon EMR to store processed data and scale out the Amazon EMR cluster as needed.
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    Use the EMR File System and Amazon S3 to store processed data and scale out the Amazon EMR cluster as needed.
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    Use Amazon DynamoDB to store processed data and scale out the Amazon EMR cluster as needed.
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    use Amazon Kinesis Data Firehose and, instead of using Amazon EMR, stream logs directly into Amazon Elasticsearch Service.
Question 12

You have been asked to handle a large data migration from multiple Amazon RDS MySQL instances to a DynamoDB table. You have been given a short amount of time to complete the data migration. What will allow you to complete this complex data processing workflow?

Select an option, then click Submit answer.

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    Create an Amazon Kinesis data stream, pipe in all of the Amazon RDS data, and direct data toward DynamoDB table
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    Write a script in you language of choice, install the script on an Amazon EC2 instance, and then use Auto Scaling groups to ensure that the latency of the mitigation pipelines never exceeds four seconds in any 15-minutes period.
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    Write a bash script to run on your Amazon RDS instance that will export data into DynamoDB
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    Create a data pipeline to export Amazon RDS data and import the data into DynamoDB