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?

• A.Create an Amazon Kinesis Data Firehose delivery stream to capture the incoming sensor data. Use an AWS Lambda transformation function to detect the known event sequence and send the SNS message.
• B.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 data. Use the Spark Streaming application to detect the known event sequence and send the SNS message.
• 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.
• D.Create an Amazon Kinesis data stream to capture the incoming sensor data and create another stream for alert messages. Set up AWS Application Auto Scaling on both. Create a Kinesis Data Analytics for Java application to detect the known event sequence, and add a message to the message stream. Configure an AWS Lambda function to poll the message stream and publish to the SNS topic.

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A manufacturing company is storing data from its operational systems in Amazon S3. The company's business analysts need to perform one-time queries of the data in Amazon S3 with Amazon Athen a. The company needs to access the Athena service from the on-premises network by using a JDBC connection. The company has created a VPC. Security policies mandate that requests to AWS services cannot traverse the internet.
Which combination of steps should a data analytics specialist take to meet these requirements? (Select TWO.)

• A.Establish an AWS Direct Connect connection between the on-premises network and the VPC.
• B.Configure the JDBC connection to connect to Athena through Amazon API Gateway.
• C.Configure the JDBC connection to use a gateway VPC endpoint for Amazon S3.
• D.Configure the JDBC connection to use an interface VPC endpoint for Athena.
• E.Deploy Athena within a private subnet.

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A banking company wants to collect large volumes of transactional data using Amazon Kinesis Data Streams for real-time analytics. The company uses PutRecord to send data to Amazon Kinesis, and has observed network outages during certain times of the day. The company wants to obtain exactly once semantics for the entire processing pipeline.
What should the company do to obtain these characteristics?

• A.Design the data producer so events are not ingested into Kinesis Data Streams multiple times.
• B.Rely on the exactly one processing semantics of Apache Flink and Apache Spark Streaming included in Amazon EMR.
• C.Rely on the processing semantics of Amazon Kinesis Data Analytics to avoid duplicate processing of events.
• D.Design the application so it can remove duplicates during processing be embedding a unique ID in each record.

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An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadat a. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync Which solution will simplify permissions management with minimal development effort?

• A.Use Amazon Cognito user pools.
• B.Manage AWS Glue and S3 permissions by using bucket policies
• C.Use AWS Lake Formation permissions
• D.Set AWS Identity and Access Management (1AM) permissions tor AWS Glue

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A company stores its sales and marketing data that includes personally identifiable information (PII) in Amazon S3. The company allows its analysts to launch their own Amazon EMR cluster and run analytics reports with the dat a. To meet compliance requirements, the company must ensure the data is not publicly accessible throughout this process. A data engineer has secured Amazon S3 but must ensure the individual EMR clusters created by the analysts are not exposed to the public internet.
Which solution should the data engineer to meet this compliance requirement with LEAST amount of effort?

• A.Create an EMR security configuration and ensure the security configuration is associated with the EMR clusters when they are created.
• B.Check the security group of the EMR clusters regularly to ensure it does not allow inbound traffic from IPv4 0.0.0.0/0 or IPv6 ::/0.
• C.Enable the block public access setting for Amazon EMR at the account level before any EMR cluster is created.
• D.Use AWS WAF to block public internet access to the EMR clusters across the board.

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