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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 data analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall data.

The amount of data that is ingested into Amazon S3 has increased to 5 PB over time. The query latency also has increased. The company needs to segment the data to reduce the amount of data that is scanned.

Which solutions will improve query performance? (Select TWO.)

AUse MySQL Workbench on an Amazon EC2 instance. Connect to Athena by using a JDBC connector. Run the query from MySQL Workbench instead of Athena directly.

BConfigure Athena to use S3 Select to load only the files of the data subset.

CCreate the data subset in Apache Parquet format each day by using the Athena CREATE TABLE AS SELECT (CTAS) statement. Query the Parquet data.

DRun a daily AWS Glue ETL job to convert the data files to Apache Parquet format and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data each day.

ECreate an S3 gateway endpoint. Configure VPC routing to access Amazon S3 through the gateway endpoint.

Question 2

A human resources company maintains a 10-node Amazon Redshift cluster to run analytics queries on the company's dat

a. The Amazon Redshift cluster contains a product table and a transactions table, and both tables have a product_sku column. The tables are over 100 GB in size. The majority of queries run on both tables.

Which distribution style should the company use for the two tables to achieve optimal query performance?

AAn EVEN distribution style for both tables

BA KEY distribution style for both tables

CAn ALL distribution style for the product table and an EVEN distribution style for the transactions table

DAn EVEN distribution style for the product table and an KEY distribution style for the transactions table

Question 3

An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical dat

a. The most recent 6 months of data will be queried frequently for analytics workloads. This data is several terabytes large. Once a month, historical data for the last 5 years must be accessible and will be joined with the more recent data. The company wants to optimize performance and cost.

Which storage solution will meet these requirements?

ACreate a read replica of the RDS database to store the most recent 6 months of data. Copy the historical data into Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3 and Amazon RDS. Run historical queries using Amazon Athena.

BUse an ETL tool to incrementally load the most recent 6 months of data into an Amazon Redshift cluster. Run more frequent queries against this cluster. Create a read replica of the RDS database to run queries on the historical data.

CIncrementally copy data from Amazon RDS to Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3. Use Amazon Athena to query the data.

DIncrementally copy data from Amazon RDS to Amazon S3. Load and store the most recent 6 months of data in Amazon Redshift. Configure an Amazon Redshift Spectrum table to connect to all historical data.

Question 4

A company is using an AWS Lambda function to run Amazon Athena queries against a cross-account AWS Glue Data Catalog. A query returns the following error:

HIVE METASTORE ERROR

The error message states that the response payload size exceeds the maximum allowed payload size. The queried table is already partitioned, and the data is stored in an

Amazon S3 bucket in the Apache Hive partition format.

Which solution will resolve this error?

AModify the Lambda function to upload the query response payload as an object into the S3 bucket. Include an S3 object presigned URL as the payload in the Lambda function response.

BRun the MSCK REPAIR TABLE command on the queried table.

CCreate a separate folder in the S3 bucket. Move the data files that need to be queried into that folder. Create an AWS Glue crawler that points to the folder instead of the S3 bucket.

DCheck the schema of the queried table for any characters that Athena does not support. Replace any unsupported characters with characters that Athena supports.

Question 5

A large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in an Amazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental dat

a. Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only.

Which solution achieves these required access patterns to minimize costs and administrative tasks?

AConsolidate all AWS accounts into one account. Create different S3 buckets for each department and move all the data from every account to the central data lake account. Migrate the individual data catalogs into a central data catalog and apply fine-grained permissions to give to each user the required access to tables and databases in AWS Glue and Amazon S3.

BKeep the account structure and the individual AWS Glue catalogs on each account. Add a central data lake account and use AWS Glue to catalog data from various accounts. Configure cross-account access for AWS Glue crawlers to scan the data in each departmental S3 bucket to identify the schema and populate the catalog. Add the senior data analysts into the central account and apply highly detailed access controls in the Data Catalog and Amazon S3.

CSet up an individual AWS account for the central data lake. Use AWS Lake Formation to catalog the cross- account locations. On each individual S3 bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls to allow senior analysts to view specific tables and columns.

DSet up an individual AWS account for the central data lake and configure a central S3 bucket. Use an AWS Lake Formation blueprint to move the data from the various buckets into the central S3 bucket. On each individual bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls for both associate and senior analysts to view specific tables and columns.

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