A retail company is building its data warehouse solution using Amazon Redshift. As a part of that effort, the company is loading hundreds of files into the fact table created in its Amazon Redshift cluster. The company wants the solution to achieve the highest throughput and optimally use cluster resources when loading data into the company's fact table.
How should the company meet these requirements?

• A.Use multiple COPY commands to load the data into the Amazon Redshift cluster.
• B.Use S3DistCp to load multiple files into the Hadoop Distributed File System (HDFS) and use an HDFS connector to ingest the data into the Amazon Redshift cluster.
• C.Use LOAD commands equal to the number of Amazon Redshift cluster nodes and load the data in parallel into each node.
• D.Use a single COPY command to load the data into the Amazon Redshift cluster.

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An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadat a. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync Which solution will simplify permissions management with minimal development effort?

• A.Set AWS Identity and Access Management (1AM) permissions tor AWS Glue
• B.Manage AWS Glue and S3 permissions by using bucket policies
• C.Use Amazon Cognito user pools.
• D.Use AWS Lake Formation permissions

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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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