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NEW QUESTION 33
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?

• A. Compress .csv files and use an INSERT statement to ingest data into Amazon Redshift.
• B. Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.
• C. Split large .csv files, then use a COPY command to load data into Amazon Redshift.
• D. Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.

Answer: C

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/c_loading-data-best-practices.html

NEW QUESTION 34
A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis.
The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs. The enrichment source is stored in an Amazon DynamoDB table.
Which solution meets the requirements for the event collection and enrichment?

• A. Use a CloudWatch Logs subscription to send the data to Amazon Kinesis Data Firehose. Use AWS Lambda to transform the data in the Kinesis Data Firehose delivery stream and enrich it with the data in the DynamoDB table. Configure Amazon S3 as the Kinesis Data Firehose delivery destination.
• B. Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use AWS Glue crawlers to catalog the logs. Set up an AWS Glue connection for the DynamoDB table and set up an AWS Glue ETL job to enrich the data. Store the enriched data in Amazon S3.
• C. Configure the application to write the logs locally and use Amazon Kinesis Agent to send the data to Amazon Kinesis Data Streams. Configure a Kinesis Data Analytics SQL application with the Kinesis data stream as the source. Join the SQL application input stream with DynamoDB records, and then store the enriched output stream in Amazon S3 using Amazon Kinesis Data Firehose.
• D. Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use Apache Spark SQL on Amazon EMR to read the logs from Amazon S3 and enrich the records with the data from DynamoDB.
  Store the enriched data in Amazon S3.

Answer: A

Explanation:
Explanation
https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html#FirehoseExample

NEW QUESTION 35
A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift cluster.
The company uses Amazon QuickSight to build dashboards and wants to secure access from its on-premises Active Directory to Amazon QuickSight.
How should the data be secured?

• A. Place Amazon QuickSight and Amazon Redshift in the security group and use an Amazon S3 endpoint to connect Amazon QuickSight to Amazon S3.
• B. Establish a secure connection by creating an S3 endpoint to connect Amazon QuickSight and a VPC endpoint to connect to Amazon Redshift.
• C. Use a VPC endpoint to connect to Amazon S3 from Amazon QuickSight and an IAM role to authenticate Amazon Redshift.
• D. Use an Active Directory connector and single sign-on (SSO) in a corporate network environment.

Answer: C

NEW QUESTION 36
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