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Free DAS-C01 Exam Braindumps (page: 15)

Page 14 of 42
PDF with 233 Questions

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?

  1. 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.
  2. 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.
  3. 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.
  4. 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.

Answer(s): A



A marketing company wants to improve its reporting and business intelligence capabilities. During the planning phase, the company interviewed the relevant stakeholders and discovered that:
The operations team reports are run hourly for the current month's data.
The sales team wants to use multiple Amazon QuickSight dashboards to show a rolling view of the last 30 days based on several categories. The sales team also wants to view the data as soon as it reaches the reporting backend. The nance team's reports are run daily for last month's data and once a month for the last 24 months of data. Currently, there is 400 TB of data in the system with an expected additional 100 TB added every month. The company is looking for a solution that is as cost- effective as possible.
Which solution meets the company's requirements?

  1. Store the last 24 months of data in Amazon Redshift. Con gure Amazon QuickSight with Amazon Redshift as the data source.
  2. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Set up an external schema and table for Amazon Redshift Spectrum. Con gure Amazon QuickSight with Amazon Redshift as the data source.
  3. Store the last 24 months of data in Amazon S3 and query it using Amazon Redshift Spectrum. Con gure Amazon QuickSight with Amazon Redshift Spectrum as the data source.
  4. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Use a long-running Amazon EMR with Apache Spark cluster to query the data as needed. Con gure Amazon QuickSight with Amazon EMR as the data source.

Answer(s): B



A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:
Daily transformations of 300 GB of data with different le formats landing in Amazon S3 at a scheduled time. One-time transformations of terabytes of archived data residing in the S3 data lake. Which combination of solutions cost-effectively meets the company's requirements for transforming the data? (Choose three.)

  1. For daily incoming data, use AWS Glue crawlers to scan and identify the schema.
  2. For daily incoming data, use Amazon Athena to scan and identify the schema.
  3. For daily incoming data, use Amazon Redshift to perform transformations.
  4. For daily incoming data, use AWS Glue work ows with AWS Glue jobs to perform transformations.
  5. For archived data, use Amazon EMR to perform data transformations.
  6. For archived data, use Amazon SageMaker to perform data transformations.

Answer(s): A,D,E



A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-real-time solution that can ingest the data securely at scale. The solution should also be able to remove the patient's protected health information (PHI) from the streaming data and store the data in durable storage.
Which solution meets these requirements with the least operational overhead?

  1. Ingest the data using Amazon Kinesis Data Streams, which invokes an AWS Lambda function using Kinesis Client Library (KCL) to remove all PHI. Write the data in Amazon S3.
  2. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Have Amazon S3 trigger an AWS Lambda function that parses the sensor data to remove all PHI in Amazon S3.
  3. Ingest the data using Amazon Kinesis Data Streams to write the data to Amazon S3. Have the data stream launch an AWS Lambda function that parses the sensor data and removes all PHI in Amazon S3.
  4. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Implement a transformation AWS Lambda function that parses the sensor data to remove all PHI.

Answer(s): D


Reference:

https://aws.amazon.com/blogs/big-data/persist-streaming-data-to-amazon-s3-using-amazon-kinesis- rehose-and-aws-lambda/






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