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Free DAS-C01 Exam Braindumps (page: 9)

Page 8 of 42
PDF with 233 Questions

A retail company's data analytics team recently created multiple product sales analysis dashboards for the average selling price per product using Amazon
QuickSight. The dashboards were created from .csv les uploaded to Amazon S3. The team is now planning to share the dashboards with the respective external product owners by creating individual users in Amazon QuickSight. For compliance and governance reasons, restricting access is a key requirement. The product owners should view only their respective product analysis in the dashboard reports. Which approach should the data analytics team take to allow product owners to view only their products in the dashboard?

  1. Separate the data by product and use S3 bucket policies for authorization.
  2. Separate the data by product and use IAM policies for authorization.
  3. Create a manifest le with row-level security.
  4. Create dataset rules with row-level security.

Answer(s): D



A company has developed an Apache Hive script to batch process data stared in Amazon S3. The script needs to run once every day and store the output in
Amazon S3. The company tested the script, and it completes within 30 minutes on a small local three-node cluster. Which solution is the MOST cost-effective for scheduling and executing the script?

  1. Create an AWS Lambda function to spin up an Amazon EMR cluster with a Hive execution step. Set KeepJobFlowAliveWhenNoSteps to false and disable the termination protection ag. Use Amazon CloudWatch Events to schedule the Lambda function to run daily.
  2. Use the AWS Management Console to spin up an Amazon EMR cluster with Python Hue. Hive, and Apache Oozie. Set the termination protection ag to true and use Spot Instances for the core nodes of the cluster. Con gure an Oozie work ow in the cluster to invoke the Hive script daily.
  3. Create an AWS Glue job with the Hive script to perform the batch operation. Con gure the job to run once a day using a time-based schedule.
  4. Use AWS Lambda layers and load the Hive runtime to AWS Lambda and copy the Hive script. Schedule the Lambda function to run daily by creating a work ow using AWS Step Functions.

Answer(s): A



A company wants to improve the data load time of a sales data dashboard. Data has been collected as .csv les and stored within an Amazon S3 bucket that is partitioned by date. The data is then loaded to an Amazon Redshift data warehouse for frequent analysis. The data volume is up to 500 GB per day.
Which solution will improve the data loading performance?

  1. Compress .csv les and use an INSERT statement to ingest data into Amazon Redshift.
  2. Split large .csv les, then use a COPY command to load data into Amazon Redshift.
  3. Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.
  4. Load the .csv les in an unsorted key order and vacuum the table in Amazon Redshift.

Answer(s): B


Reference:

https://aws.amazon.com/blogs/big-data/using-amazon-redshift-spectrum-amazon-athena-and-aws-glue-with-node-js-in-production/



A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to execute. The company needs to optimize query execution and avoid any downtime.
What is the MOST cost-effective solution?

  1. Enable concurrency scaling in the workload management (WLM) queue.
  2. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.
  3. Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.
  4. Use a snapshot, restore, and resize operation. Switch to the new target cluster.

Answer(s): A






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