AWS Certified Data Analytics - Specialty practice questions, page 7 of 7, with answer review and explanations.
Question 31
A company wants to improve the data load time of a sales data dashboard. Data has been collected as .csv files 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?
Compress .csv files and use an INSERT statement to ingest data into Amazon Redshift.
Split large .csv files, then use a COPY command to load data into Amazon Redshift.
Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.
Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.
Question 32
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?
Enable concurrency scaling in the workload management (WLM) queue.
Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.
Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.
Use a snapshot, restore, and resize operation. Switch to the new target cluster.