A large financial company is running its ETL process. Part of this process is to move data from Amazon S3 into an Amazon Redshift cluster. The company wants to use the most cost-efficient method to load the dataset into Amazon Redshift.
Which combination of steps would meet these requirements? (Choose two.)

• A.Use the COPY command with the manifest file to load data into Amazon Redshift.
• B.Use Amazon Redshift Spectrum to query files from Amazon S3.
• C.Use S3DistCp to load files into Amazon Redshift.
• D.Use the UNLOAD command to upload data into Amazon Redshift.
• E.Use temporary staging tables during the loading process.

Comment: *

Name: *

Email: *

Verification: *

A company is hosting an enterprise reporting solution with Amazon Redshift. The application provides reporting capabilities to three main groups: an executive group to access financial reports, a data analyst group to run long-running ad-hoc queries, and a data engineering group to run stored procedures and ETL processes. The executive team requires queries to run with optimal performance. The data engineering team expects queries to take minutes.
Which Amazon Redshift feature meets the requirements for this task?

• A.Concurrency scaling
• B.Short query acceleration (SQA)
• C.Workload management (WLM)
• D.Materialized views

Comment: *

Name: *

Email: *

Verification: *

A media company has been performing analytics on log data generated by its applications. There has been a recent increase in the number of concurrent analytics jobs running, and the overall performance of existing jobs is decreasing as the number of new jobs is increasing. The partitioned data is stored in Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) and the analytic processing is performed on Amazon EMR clusters using the EMR File System (EMRFS) with consistent view enabled. A data analyst has determined that it is taking longer for the EMR task nodes to list objects in Amazon S3.
Which action would MOST likely increase the performance of accessing log data in Amazon S3?

• A.Redeploy the EMR clusters that are running slowly to a different Availability Zone.
• B.Use a lifecycle policy to change the S3 storage class to S3 Standard for the log data.
• C.Use a hash function to create a random string and add that to the beginning of the object prefixes when storing the log data in Amazon S3.
• D.Increase the read capacity units (RCUs) for the shared Amazon DynamoDB table.

Comment: *

Name: *

Email: *

Verification: *

A reseller that has thousands of AWS accounts receives AWS Cost and Usage Reports in an Amazon S3 bucket The reports are delivered to the S3 bucket in the following format
<examp/e-reporT-prefix>/<examp/e-report-rtame>/yyyymmdd-yyyymmdd/<examp/e-report-name> parquet An AWS Glue crawler crawls the S3 bucket and populates an AWS Glue Data Catalog with a table Business analysts use Amazon Athena to query the table and create monthly summary reports for the AWS accounts The business analysts are experiencing slow queries because of the accumulation of reports from the last 5 years The business analysts want the operations team to make changes to improve query performance Which action should the operations team take to meet these requirements?

• A.Partition the data by account ID, year, and month
• B.Partition the data by date and account ID
• C.Change the file format to csv.zip.
• D.Partition the data by month and account ID

Comment: *

Name: *

Email: *

Verification: *

A transportation company uses IoT sensors attached to trucks to collect vehicle data for its global delivery fleet. The company currently sends the sensor data in small .csv files to Amazon S3. The files are then loaded into a 10-node Amazon Redshift cluster with two slices per node and queried using both Amazon Athena and Amazon Redshift. The company wants to optimize the files to reduce the cost of querying and also improve the speed of data loading into the Amazon Redshift cluster.
Which solution meets these requirements?

• A.Use Amazon EMR to convert each .csv file to Apache Avro. COPY the files into Amazon Redshift and query the file with Athena from Amazon S3.
• B.Use AWS Glue to convert the files from .csv to a single large Apache ORC file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• C.Use AWS Glue to convert all the files from .csv to a single large Apache Parquet file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• D.Use AWS Glue to convert the files from .csv to Apache Parquet to create 20 Parquet files. COPY the files into Amazon Redshift and query the files with Athena from Amazon S3.

Comment: *

Name: *

Email: *

Verification: *