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Associate-DEA-C01_12

8 min read

Questions and Answers

Question Ijuy4KKaYfmUlAAs6IzB

Question

A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.

Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.

Which factors could have caused the reordering system to receive duplicated data? (Choose two.)

Choices

  • A: The producer experienced network-related timeouts.
  • B: The stream’s value for the IteratorAgeMilliseconds metric was too high.
  • C: There was a change in the number of shards, record processors, or both.
  • D: The AggregationEnabled configuration property was set to true.
  • E: The max_records configuration property was set to a number that was too high.

Question xi9dI2CJANuDzaVKXp3p

Question

A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularly proliferated to the S3 bucket. The data includes files that are in multiple formats. The data engineer needs to automate the transfer process and must schedule the process to run periodically. Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way?

Choices

  • A: AWS DataSync
  • B: AWS Glue
  • C: AWS Direct Connect
  • D: Amazon S3 Transfer Acceleration

Question lYc77WFV6k4IUz7q6KHt

Question

An ecommerce company operates a complex order fulfilment process that spans several operational systems hosted in AWS. Each of the operational systems has a Java Database Connectivity (JDBC)-compliant relational database where the latest processing state is captured.

The company needs to give an operations team the ability to track orders on an hourly basis across the entire fulfillment process.

Which solution will meet these requirements with the LEAST development overhead?

Choices

  • A: Use AWS Glue to build ingestion pipelines from the operational systems into Amazon Redshift Build dashboards in Amazon QuickSight that track the orders.
  • B: Use AWS Glue to build ingestion pipelines from the operational systems into Amazon DynamoDBuild dashboards in Amazon QuickSight that track the orders.
  • C: Use AWS Database Migration Service (AWS DMS) to capture changed records in the operational systems. Publish the changes to an Amazon DynamoDB table in a different AWS region from the source database. Build Grafana dashboards that track the orders.
  • D: Use AWS Database Migration Service (AWS DMS) to capture changed records in the operational systems. Publish the changes to an Amazon DynamoDB table in a different AWS region from the source database. Build Amazon QuickSight dashboards that track the orders.

Question CUuGCxeHOl7aKo8miAEm

Question

A data engineer needs to use Amazon Neptune to develop graph applications.

Which programming languages should the engineer use to develop the graph applications? (Choose two.)

Choices

  • A: Gremlin
  • B: SQL
  • C: ANSI SQL
  • D: SPARQL
  • E: Spark SQL

Question sk217wzPN9YJHjd5pp8r

Question

A mobile gaming company wants to capture data from its gaming app. The company wants to make the data available to three internal consumers of the data. The data records are approximately 20 KB in size.

The company wants to achieve optimal throughput from each device that runs the gaming app. Additionally, the company wants to develop an application to process data streams. The stream-processing application must have dedicated throughput for each internal consumer.

Which solution will meet these requirements?

Choices

  • A: Configure the mobile app to call the PutRecords API operation to send data to Amazon Kinesis Data Streams. Use the enhanced fan-out feature with a stream for each internal consumer.
  • B: Configure the mobile app to call the PutRecordBatch API operation to send data to Amazon Kinesis Data Firehose. Submit an AWS Support case to turn on dedicated throughput for the company’s AWS account. Allow each internal consumer to access the stream.
  • C: Configure the mobile app to use the Amazon Kinesis Producer Library (KPL) to send data to Amazon Kinesis Data Firehose. Use the enhanced fan-out feature with a stream for each internal consumer.
  • D: Configure the mobile app to call the PutRecords API operation to send data to Amazon Kinesis Data Streams. Host the stream-processing application for each internal consumer on Amazon EC2 instances. Configure auto scaling for the EC2 instances.

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