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 media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:
* Daily transformations of 300 GB of data with different file formats landing in Amazon S3 at a scheduled time.
* One-time transformations of terabytes of archived data residing in the S3 data lake.
Which combination of solutions cost-effectively meets the company's requirements for transforming the data?
(Choose three.)

• A.For archived data, use Amazon SageMaker to perform data transformations.
• B.For daily incoming data, use Amazon Redshift to perform transformations.
• C.For archived data, use Amazon EMR to perform data transformations.
• D.For daily incoming data, use Amazon Athena to scan and identify the schema.
• E.For daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform transformations.
• F.For daily incoming data, use AWS Glue crawlers to scan and identify the schema.

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A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3. The company's analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall dat a. The amount of data that is ingested into Amazon S3 has increased substantially over time, and the query latency also has increased.
Which solutions could the company implement to improve query performance? (Choose two.)

• A.Use MySQL Workbench on an Amazon EC2 instance, and connect to Athena by using a JDBC or ODBC connector. Run the query from MySQL Workbench instead of Athena directly.
• B.Use Athena to extract the data and store it in Apache Parquet format on a daily basis. Query the extracted data.
• C.Run a daily AWS Glue ETL job to convert the data files to Apache Parquet and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data on a daily basis.
• D.Run a daily AWS Glue ETL job to compress the data files by using the .gzip format. Query the compressed data.
• E.Run a daily AWS Glue ETL job to compress the data files by using the .lzo format. Query the compressed data.

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A company uses Amazon Elasticsearch Service (Amazon ES) to store and analyze its website clickstream data. The company ingests 1 TB of data daily using Amazon Kinesis Data Firehose and stores one day's worth of data in an Amazon ES cluster.
The company has very slow query performance on the Amazon ES index and occasionally sees errors from Kinesis Data Firehose when attempting to write to the index. The Amazon ES cluster has 10 nodes running a single index and 3 dedicated master nodes. Each data node has 1.5 TB of Amazon EBS storage attached and the cluster is configured with 1,000 shards. Occasionally, JVMMemoryPressure errors are found in the cluster logs.
Which solution will improve the performance of Amazon ES?

• A.Increase the memory of the Amazon ES master nodes.
• B.Increase the number of Amazon ES shards for the index.
• C.Decrease the number of Amazon ES data nodes.
• D.Decrease the number of Amazon ES shards for the index.

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A marketing company is storing its campaign response data in Amazon S3. A consistent set of sources has generated the data for each campaign. The data is saved into Amazon S3 as .csv files. A business analyst will use Amazon Athena to analyze each campaign's data. The company needs the cost of ongoing data analysis with Athena to be minimized.
Which combination of actions should a data analytics specialist take to meet these requirements? (Choose two.)

• A.Convert the .csv files to Apache Parquet.
• B.Convert the .csv files to Apache Avro.
• C.Partition the data by campaign.
• D.Partition the data by source.
• E.Compress the .csv files.

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