1. An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored inAmazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well- functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.Which solution meets these requirements?

A) Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function. Perform the join with AWS Glue ETL scripts.
B) Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.
C) Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
D) Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.



2. A data analyst is using Amazon QuickSight for data visualization across multiple datasets generated by applications. Each application stores files within a separate Amazon S3 bucket. AWS Glue Data Catalog is used as a central catalog across all application data in Amazon S3. A new application stores its data within a separate S3 bucket. After updating the catalog to include the new application data source, the data analyst created a new Amazon QuickSight data source from an Amazon Athena table, but the import into SPICE failed.How should the data analyst resolve the issue?

A) Edit the permissions for the AWS Glue Data Catalog from within the Amazon QuickSight console.
B) Edit the permissions for the new S3 bucket from within the Amazon QuickSight console.
C) Edit the permissions for the AWS Glue Data Catalog from within the AWS Glue console.
D) Edit the permissions for the new S3 bucket from within the S3 console.



3. A team of data scientists plans to analyze market trend data for their company's new investment strategy. The trend data comes from five different data sources in large volumes. The team wants to utilize Amazon Kinesis to support their use case. The team uses SQL-like queries to analyze trends and wants to send notifications based on certain significant patterns in the trends. Additionally, the data scientists want to save the data to Amazon S3 for archival and historical re- processing, and use AWS managed services wherever possible. The team wants to implement the lowest-cost solution.Which solution meets these requirements?

A) Publish data to one Kinesis data stream. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
B) Publish data to one Kinesis data stream. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
C) Publish data to two Kinesis data streams. Deploy Kinesis Data Analytics to the first stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
D) Publish data to two Kinesis data streams. Deploy a custom application using the Kinesis Client Library (KCL) to the first stream for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.



4. A company currently uses Amazon Athena to query its global datasets. The regional data is stored in Amazon S3 in the us-east-1 and us-west-2 Regions. The data is not encrypted. To simplify the query process and manage it centrally, the company wants to use Athena in us-west-2 to query data from Amazon S3 in bothRegions. The solution should be as low-cost as possible.What should the company do to achieve this goal?

A) Use AWS DMS to migrate the AWS Glue Data Catalog from us-east-1 to us-west-2. Run Athena queries in us-west-2.
B) Run the AWS Glue crawler in us-west-2 to catalog datasets in all Regions. Once the data is crawled, run Athena queries in us-west-2.
C) Enable cross-Region replication for the S3 buckets in us-east-1 to replicate data in us-west-2. Once the data is replicated in us-west-2, run the AWS Glue crawler there to update the AWS Glue Data Catalog in us-west-2 and run Athena queries.
D) Update AWS Glue resource policies to provide us-east-1 AWS Glue Data Catalog access to us-west-2. Once the catalog in us-west-2 has access to the catalog in us-east-1, run Athena queries in us-west-2.



5. A large company receives files from external parties in Amazon EC2 throughout the day. At the end of the day, the files are combined into a single file, compressed into a gzip file, and uploaded to Amazon S3. The total size of all the files is close to 100 GB daily. Once the files are uploaded to Amazon S3, anAWS Batch program executes a COPY command to load the files into an Amazon Redshift cluster.Which program modification will accelerate the COPY process?

A) Upload the individual files to Amazon S3 and run the COPY command as soon as the files become available.
B) Split the number of files so they are equal to a multiple of the number of slices in the Amazon Redshift cluster. Gzip and upload the files to Amazon S3. Run the COPY command on the files.
C) Split the number of files so they are equal to a multiple of the number of compute nodes in the Amazon Redshift cluster. Gzip and upload the files to Amazon S3. Run the COPY command on the files.
D) Apply sharding by breaking up the files so the distkey columns with the same values go to the same file. Gzip and upload the sharded files to Amazon S3. Run the COPY command on the files.