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Free DAS-C01 Exam Braindumps (page: 5)

Page 4 of 42
PDF with 233 Questions

A company is planning to do a proof of concept for a machine learning (ML) project using Amazon SageMaker with a subset of existing on- premises data hosted in the company's 3 TB data warehouse. For part of the project, AWS Direct Connect is established and tested. To prepare the data for ML, data analysts are performing data curation. The data analysts want to perform multiple step, including mapping, dropping null elds, resolving choice, and splitting elds. The company needs the fastest solution to curate the data for this project.
Which solution meets these requirements?

  1. Ingest data into Amazon S3 using AWS DataSync and use Apache Spark scrips to curate the data in an Amazon EMR cluster. Store the curated data in Amazon S3 for ML processing.
  2. Create custom ETL jobs on-premises to curate the data. Use AWS DMS to ingest data into Amazon S3 for ML processing.
  3. Ingest data into Amazon S3 using AWS DMS. Use AWS Glue to perform data curation and store the data in Amazon S3 for ML processing.
  4. Take a full backup of the data store and ship the backup les using AWS Snowball. Upload Snowball data into Amazon S3 and schedule data curation jobs using AWS Batch to prepare the data for ML.

Answer(s): C



A US-based sneaker retail company launched its global website. All the transaction data is stored in Amazon RDS and curated historic transaction data is stored in Amazon Redshift in the us-east-1 Region. The business intelligence (BI) team wants to enhance the user experience by providing a dashboard for sneaker trends.
The BI team decides to use Amazon QuickSight to render the website dashboards. During development, a team in Japan provisioned Amazon QuickSight in ap- northeast-1. The team is having di culty connecting Amazon QuickSight from ap-northeast-1 to Amazon Redshift in us-east-1.
Which solution will solve this issue and meet the requirements?

  1. In the Amazon Redshift console, choose to con gure cross-Region snapshots and set the destination Region as ap-northeast-1. Restore the Amazon Redshift Cluster from the snapshot and connect to Amazon QuickSight launched in ap-northeast-1.
  2. Create a VPC endpoint from the Amazon QuickSight VPC to the Amazon Redshift VPC so Amazon QuickSight can access data from Amazon Redshift.
  3. Create an Amazon Redshift endpoint connection string with Region information in the string and use this connection string in Amazon QuickSight to connect to Amazon Redshift.
  4. Create a new security group for Amazon Redshift in us-east-1 with an inbound rule authorizing access from the appropriate IP address range for the Amazon QuickSight servers in ap-northeast-1.

Answer(s): D



An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in
Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well- functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.
Which solution meets these requirements?

  1. Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function. Perform the join with AWS Glue ETL scripts.
  2. Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.
  3. Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
  4. Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.

Answer(s): C



A data analyst is using Amazon QuickSight for data visualization across multiple datasets generated by applications. Each application stores les within a separate Amazon S3 bucket. AWS Glue Data Catalog is used as a central catalog across all application data in Amazon S3. A new application stores its data within a separate S3 bucket. After updating the catalog to include the new application data source, the data analyst created a new Amazon QuickSight data source from an Amazon Athena table, but the import into SPICE failed.
How should the data analyst resolve the issue?

  1. Edit the permissions for the AWS Glue Data Catalog from within the Amazon QuickSight console.
  2. Edit the permissions for the new S3 bucket from within the Amazon QuickSight console.
  3. Edit the permissions for the AWS Glue Data Catalog from within the AWS Glue console.
  4. Edit the permissions for the new S3 bucket from within the S3 console.

Answer(s): B


Reference:

https://aws.amazon.com/blogs/big-data/harmonize-query-and-visualize-data-from-various-providers-using-aws-glue-amazon-athena-and- amazon- quicksight/






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