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

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A large ride-sharing company has thousands of drivers globally serving millions of unique customers every day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema includes the following tables.
* A trips fact table for information on completed rides.
* A drivers dimension table for driver profiles.
* A customers fact table holding customer profile information.
The company analyzes trip details by date and destination to examine profitability by region. The drivers data rarely changes. The customers data frequently changes.
What table design provides optimal query performance?

• A.Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.
• B.Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables.
• C.Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables.
• D.Use DISTSTYLE EVEN for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table.
  Use DISTSTYLE EVEN for the customers table.

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A retail company wants to use Amazon QuickSight to generate dashboards for web and in-store sales. A group of 50 business intelligence professionals will develop and use the dashboards. Once ready, the dashboards will be shared with a group of 1,000 users.
The sales data comes from different stores and is uploaded to Amazon S3 every 24 hours. The data is partitioned by year and month, and is stored in Apache Parquet format. The company is using the AWS Glue Data Catalog as its main data catalog and Amazon Athena for querying. The total size of the uncompressed data that the dashboards query from at any point is 200 GB.
Which configuration will provide the MOST cost-effective solution that meets these requirements?

• A.Use QuickSight Enterprise edition. Configure 1 administrator and 1,000 reader users. Configure an S3 data source and import the data into SPICE. Automatically refresh every 24 hours.
• B.Load the data into an Amazon Redshift cluster by using the COPY command. Configure 50 author users and 1,000 reader users. Use QuickSight Enterprise edition. Configure an Amazon Redshift data source with a direct query option.
• C.Use QuickSight Enterprise edition. Configure 50 author users and 1,000 reader users. Configure an Athena data source and import the data into SPICE. Automatically refresh every 24 hours.
• D.Use QuickSight Standard edition. Configure 50 author users and 1,000 reader users. Configure an Athena data source with a direct query option.

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A company that produces network devices has millions of users. Data is collected from the devices on an hourly basis and stored in an Amazon S3 data lake.
The company runs analyses on the last 24 hours of data flow logs for abnormality detection and to troubleshoot and resolve user issues. The company also analyzes historical logs dating back 2 years to discover patterns and look for improvement opportunities.
The data flow logs contain many metrics, such as date, timestamp, source IP, and target IP. There are about 10 billion events every day.
How should this data be stored for optimal performance?

• A.In compressed .csv partitioned by date and sorted by source IP
• B.In Apache ORC partitioned by date and sorted by source IP
• C.In Apache Parquet partitioned by source IP and sorted by date
• D.In compressed nested JSON partitioned by source IP and sorted by date

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A company is migrating from an on-premises Apache Hadoop cluster to an Amazon EMR cluster. The cluster runs only during business hours. Due to a company requirement to avoid intraday cluster failures, the EMR cluster must be highly available. When the cluster is terminated at the end of each business day, the data must persist.
Which configurations would enable the EMR cluster to meet these requirements? (Choose three.)

• A.EMR File System (EMRFS) for storage
• B.Hadoop Distributed File System (HDFS) for storage
• C.AWS Glue Data Catalog as the metastore for Apache Hive
• D.MySQL database on the master node as the metastore for Apache Hive
• E.Multiple master nodes in a single Availability Zone
• F.Multiple master nodes in multiple Availability Zones

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