A company collects and transforms data files from third-party providers by using an on-premises SFTP server. The company uses a Python script to transform the data.
The company wants to reduce the overhead of maintaining the SFTP server and storing large amounts of data on premises. However, the company does not want to change the existing upload process for the third-party providers.
Which solution will meet these requirements with the LEAST development effort?
A. Deploy the Python script on an Amazon EC2 instance. Install a third-party SFTP server on the EC2 instance. Schedule the script to run periodically on the EC2 instance to perform a data transformation on new files. Copy the transformed files to Amazon S3.
B. Create an Amazon S3 bucket that includes a separate prefix for each provider. Provide the S3 URL to each provider for its respective prefix. Instruct the providers to use the S3 COPY command to upload data. Configure an AWS Lambda function that transforms the data when new files are uploaded.
C. Use AWS Transfer Family to create an SFTP server that includes a publicly accessible endpoint. Configure the new server to use Amazon S3 storage. Change the server name to match the name of the on-premises SFTP server. Schedule a Python shell job in AWS Glue to use the existing Python script to run periodically and transform the uploaded files.
D. Use AWS Transfer Family to create an SFTP server that includes a publicly accessible endpoint. Configure the new server to use Amazon S3 storage. Change the server name to match the name of the on-premises SFTP server. Use AWS Data Pipeline to schedule a transient Amazon EMR cluster with an Apache Spark step to periodically transform the files.
A company has a production AWS account that runs production workloads. The company created a new security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs. The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?
A. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
B. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
C. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
D. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.
A company stores transaction data in an Amazon Aurora PostgreSQL DB cluster. Some of the data is sensitive. A data analytics specialist must implement a solution to classify the data in the database and create a report. Which combination of steps will meet this requirement with the LEAST development effort? (Choose two.)
A. Create an Amazon S3 bucket. Export an Aurora DB cluster snapshot to the bucket.
B. Create an Amazon S3 bucket. Create an AWS Lambda function to run Amazon Athena federated queries on the database and to store the output as S3 objects in Apache Parquet format.
C. Create an Amazon S3 bucket. Create an AWS Lambda function to run Amazon Athena federated queries on the database and to store the output as S3 objects in CSV format.
D. Create an AWS Lambda function to analyze the bucket contents and create a report.
E. Create an Amazon Macie job to analyze the bucket contents and create a report.
A data analytics specialist is building a solution to securely collect and store data from multiple applications for analytics. The solution must store the data across multiple Availability Zones less than 2 minutes after collecting the data. The solution also must provide a secure way to authenticate and authorize the source application users.
Which solution will meet these requirements with the LEAST operational overhead?
A. Authenticate by using an Amazon Cognito user pool that is authorized to write to an Amazon API Gateway REST API. Configure the API as a proxy for an Amazon Kinesis Data Firehose delivery stream that has an Amazon S3 destination.
B. Write data to an Amazon S3 bucket by using the AWS SDK. Configure a bucket policy that limits writes to specific IAM roles.
C. Create an IAM access key that is authorized to write to an Amazon Kinesis Data Firehose delivery stream that has an Amazon S3 destination. Embed the access key into the applications.
D. Call a REST-based service such as Amazon API Gateway that uses a custom authentication service to store data on a Kubernetes cluster.
A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-real-time solution that can ingest the data securely at scale. The solution should also be able to remove the patient's protected health information (PHI) from the streaming data before storing the data in durable storage.
Which solution meets these requirements with the LEAST operational overhead?
A. Ingest the data by using Amazon Kinesis Data Streams. Process the data by using Amazon EC2 instances that use Amazon Kinesis Client Library (KCL) and custom logic to remove all PHI from the data. Write the data to Amazon S3.
B. Ingest the data by using Amazon Kinesis Data Firehose and write the data to Amazon S3. Have Amazon S3 invoke an AWS Lambda function that removes all PHI.
C. Ingest the data by using Amazon Kinesis Data Streams to write the data to Amazon S3. Have Amazon S3 invoke an AWS Lambda function that removes all PHI.
D. Ingest the data by using Amazon Kinesis Data Firehose. Invoke a Kinesis Data Firehose data transformation by using an AWS Lambda function to remove all PHI. Configure Kinesis Data Firehose so that Amazon S3 is the destination.
A company tracks its sales opportunities in Salesforce. The company is using stacked bar charts inside Salesforce to visualize quarterly trends of open, lost, and closed sales opportunities by each business line. The company wants to host these charts in the AWS Cloud outside Salesforce so that employees do not need a Salesforce license to view the charts.
Which solution will meet this requirement with the LEAST development effort?
A. Use Amazon AppFlow to schedule a nightly export of the data in CSV format from Salesforce to Amazon S3. Import the data from the file on Amazon S3 into SPICE. Build the stacked bar chart in Amazon QuickSight.
B. Schedule a nightly script that uses the Salesforce Bulk API to run on an Amazon EC2 instance and copy data in CSV format to Amazon S3. Import the data from the file on Amazon S3 into SPICE. Build the stacked bar chart in Amazon QuickSight.
C. Use AWS Data Pipeline to schedule a nightly export of the data in CSV format from Salesforce to Amazon S3. Import the data from the file on Amazon S3 into SPICE. Build the stacked bar chart in Amazon QuickSight.
D. Use Amazon AppFlow to schedule a nightly export of the data in Apache Parquet format from Salesforce to Amazon S3. Import the data from the file on Amazon S3 into SPICE. Build the stacked bar chart in Amazon QuickSight.
A telecommunications company stores its call records as JSON files in an Amazon S3 bucket. The company uses Amazon Athena to query the records and wants to improve query performance. The data is stored in data records that have up to 300 different columns. The most common query uses a subset of only 12 of the columns.
Which solution will improve the query performance?
A. Convert the data to Apache Parquet files by using native JSON Serializer/Deserializer (SerDe) libraries.
B. Convert the data to Apache Parquet files by using Amazon EMR. Compress the files by using Snappy.
C. Convert the data to Apache Parquet files by using Amazon EMR. Compress the files by using gzip.
D. Convert the data to Apache Avro files by using Athena. Compress the files by using bzip2.
A company uses Amazon S3 for encrypted cloud storage and uses Amazon Redshift for its cloud data warehouse. The company must encrypt the S3 data while still allowing the Amazon Redshift COPY command to access the data. Which solution will meet these requirements?
A. Use server-side encryption with Amazon S3 managed keys (SSE-S3).
B. Use server-side encryption with customer-provided keys (SSE-C).
C. Use client-side encryption with an AWS Key Management Service (AWS KMS) key.
D. Use client-side encryption with a customer-provided asymmetric root key.
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? (Choose two.)
A. Use 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.
B. Configure Athena to use S3 Select to load only the files of the data subset.
C. Create the data subset in Apache Parquet format each day by using the Athena CREATE TABLE AS SELECT (CTAS) statement. Query the Parquet data.
D. Run 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.
E. Create an S3 gateway endpoint. Configure VPC routing to access Amazon S3 through the gateway endpoint.
A global technology company is creating dashboards to visualize time series data. The data is ingested into Amazon Managed Streaming for Apache Kafka (Amazon MSK) and consumed by a customized data pipeline. The data is then written to Amazon Keyspaces (for Apache Cassandra), Amazon OpenSearch Service, and Avro files in Amazon S3. The dashboards must provide near-real-time results.
Which solution meets these requirements?
A. Create OpenSearch Service dashboards by using the data from OpenSearch Service with the desired analyses and visualizations.
B. Use Amazon Athena with an Apache Hive metastore to query the Avro files in Amazon S3. Connect Tableau to Athena and create dashboards and visualizations in Tableau.
C. Use Amazon Athena and configure Amazon Keyspaces as the data catalog. Connect Amazon QuickSight to Athena to create the desired analyses and visualizations.
D. Use AWS Glue to catalog the data and Amazon Athena to query the Avro files in Amazon S3. Connect Amazon QuickSight to Athena to create the desired analyses and visualizations.
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