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1. A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

A) Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.
B) Turn on concurrency scaling for the daily usage quota for the Redshift cluster.
C) Turn on concurrency scaling in the settings during the creation of and new Redshift cluster.
D) Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.

2. A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.
Which solution will meet these requirements with the LEAST ongoing maintenance?

A) Use the DynamoDB TTL feature to automatically expire data based on timestamps.
B) Configure a scheduled Amazon EventBridge rule to invoke an AWS Lambda function to check for data that is older than 1 month. Configure the Lambda function to delete old data.
C) Use an AWS Lambda function to periodically scan the DynamoDB table for data that is older than 1 month. Configure the Lambda function to delete old data.
D) Configure a stream on the DynamoDB table to invoke an AWS Lambda function. Configure the Lambda function to delete data in the table that is older than 1 month.

3. A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?

A) Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.
B) Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.
C) Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
D) Confiqure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe- Write a SQL SELECT statement on the dataframe to query the required column.

4. A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?

A) Wait state
B) Parallel state
C) Map state
D) Choice state

5. A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time.
The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds.
Which solution will meet these requirements with the LEAST operational overhead?

A) Use Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data. Use AWS Glue to store the data in Amazon RDS for querying.
B) Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.
C) Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.
D) Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.

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