A regional energy company collects voltage data from sensors attached to buildings. To address any known dangerous conditions, the company wants to be alerted when a sequence of two voltage drops is detected within 10 minutes of a voltage spike at the same building. It is important to ensure that all messages are delivered as quickly as possible. The system must be fully managed and highly available. The company also needs a solution that will automatically scale up as it covers additional cites with this monitoring feature. The alerting system is subscribed to an Amazon SNS topic for remediation.
Which solution meets these requirements?

1. A. Create an Amazon Managed Streaming for Kafka cluster to ingest the data, and use an Apache Spark Streaming with Apache Kafka consumer API in an automatically scaled Amazon EMR cluster to process the incoming dat
2. B. Use the Spark Streaming application to detect the known event sequence and send the SNS message.
3. C. Create a REST-based web service using Amazon API Gateway in front of an AWS Lambda function.Create an Amazon RDS for PostgreSQL database with sufficient Provisioned IOPS (PIOPS). In the Lambda function, store incoming events in the RDS database and query the latest data to detect the known event sequence and send the SNS message.
4. D. Create an Amazon Kinesis Data Firehose delivery stream to capture the incoming sensor dat
5. E. Use an AWS Lambda transformation function to detect the known event sequence and send the SNS message.
6. F. Create an Amazon Kinesis data stream to capture the incoming sensor data and create another stream for alert message
7. G. Set up AWS Application Auto Scaling on bot
8. H. Create a Kinesis Data Analytics for Java application to detect the known event sequence, and add a message to the message strea
9. I. Configure an AWS Lambda function to poll the message stream and publish to the SNS topic.

Correct Answer: D

A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis. The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs. The enrichment source is stored in an Amazon DynamoDB table.
Which solution meets the requirements for the event collection and enrichment?

1. A. Use a CloudWatch Logs subscription to send the data to Amazon Kinesis Data Firehos
2. B. Use AWS Lambda to transform the data in the Kinesis Data Firehose delivery stream and enrich it with the data inthe DynamoDB tabl
3. C. Configure Amazon S3 as the Kinesis Data Firehose delivery destination.
4. D. Export the raw logs to Amazon S3 on an hourly basis using the AWS CL
5. E. Use AWS Glue crawlers to catalog the log
6. F. Set up an AWS Glue connection for the DynamoDB table and set up an AWS Glue ETL job to enrich the dat
7. G. Store the enriched data in Amazon S3.
8. H. Configure the application to write the logs locally and use Amazon Kinesis Agent to send the data to Amazon Kinesis Data Stream
9. I. Configure a Kinesis Data Analytics SQL application with the Kinesis data stream as the sourc
10. J. Join the SQL application input stream with DynamoDB records, and then store the enriched output stream in Amazon S3 using Amazon Kinesis Data Firehose.
11. K. Export the raw logs to Amazon S3 on an hourly basis using the AWS CL
12. L. Use Apache Spark SQL on Amazon EMR to read the logs from Amazon S3 and enrich the records with the data from DynamoD
13. M. Store the enriched data in Amazon S3.

Correct Answer: A
https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html#FirehoseExample

A data analyst runs a large number of data manipulation language (DML) queries by using Amazon Athena with the JDBC driver. Recently, a query failed after It ran for 30 minutes. The query returned the following message Java.sql.SGLException: Query timeout
The data analyst does not immediately need the query results However, the data analyst needs a long-term solution for this problem
Which solution will meet these requirements?

1. A. Split the query into smaller queries to search smaller subsets of data.
2. B. In the settings for Athena, adjust the DML query timeout limit
3. C. In the Service Quotas console, request an increase for the DML query timeout
4. D. Save the tables as compressed .csv files

Correct Answer: A

A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to execute. The company needs to optimize query execution and avoid any downtime.
What is the MOST cost-effective solution?

1. A. Enable concurrency scaling in the workload management (WLM) queue.
2. B. Add more nodes using the AWS Management Console during peak hour
3. C. Set the distribution style to ALL.
4. D. Use elastic resize to quickly add nodes during peak time
5. E. Remove the nodes when they are not needed.
6. F. Use a snapshot, restore, and resize operatio
7. G. Switch to the new target cluster.

Correct Answer: A
https://docs.aws.amazon.com/redshift/latest/dg/cm-c-implementing-workload-management.html

A company has a business unit uploading .csv files to an Amazon S3 bucket. The company’s data platform team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job writes processed data from the created tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creating the Amazon Redshift table appropriately. When the AWS Glue job is rerun for any reason in a day, duplicate records are introduced into the Amazon Redshift table.
Which solution will update the Redshift table without duplicates when jobs are rerun?

1. A. Modify the AWS Glue job to copy the rows into a staging tabl
2. B. Add SQL commands to replace the existing rows in the main table as postactions in the DynamicFrameWriter class.
3. C. Load the previously inserted data into a MySQL database in the AWS Glue jo
4. D. Perform an upsert operation in MySQL, and copy the results to the Amazon Redshift table.
5. E. Use Apache Spark’s DataFrame dropDuplicates() API to eliminate duplicates and then write the data to Amazon Redshift.
6. F. Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.

Correct Answer: A
https://aws.amazon.com/premiumsupport/knowledge-center/sql-commands-redshift-glue-job/ See the section Merge an Amazon Redshift table in AWS Glue (upsert)