DEA-C01

You are designing a system for a financial company that will analyze market data and provide insights. The system should be able to handle large volumes of data and support a high number of concurrent users running complex SQL queries. Which combination of AWS services would be the most suitable to meet these requirements?. Use Amazon Kinesis Data Streams for data ingestion, AWS Glue for data transformation, and Amazon Redshift with Concurrency Scaling enabled for data storage and analysis. Use Amazon Kinesis Data Streams for data ingestion, Amazon EMR for data transformation, and Amazon RDS with Read Replicas for data storage and analysis. Use AWS DMS for data ingestion, AWS Glue for data transformation, and Amazon Aurora with Read Replicas for data storage and analysis. Use Amazon Kinesis Data Firehose for data ingestion, AWS Glue for data transformation, and Amazon DynamoDB with On-Demand capacity mode for data storage and analysis.

Your company is building a new big data platform on AWS. The platform will ingest large amounts of structured data and store it in Amazon S3 for query processing with Amazon Redshift. The data will be frequently accessed, and query performance is a top priority. Which combination of data layout, schema, structure, format, and compression strategy would be the most appropriate?. Store data in Amazon S3 without partitioning, convert data to Parquet format, and apply LZO compression. Store data in Amazon S3 without partitioning, convert data to CSV format, and apply Snappy compression. Store data in Amazon S3 using Hive-style partitioning, convert data to Parquet format, and apply Snappy compression. Store data in Amazon S3 using Hive-style partitioning, convert data to CSV format, and apply GZIP compression.

A finance company is receiving real-time trading data in a nested JSON format, which it stores in Amazon Kinesis Data Streams. The data analysts need to analyze this data in combination with historical trading data stored in an Amazon Redshift cluster. The analysts want a solution that is real-time, cost-effective, and automated. Which solution meets these requirements?. Use Amazon Kinesis Data Analytics for Apache Flink to process the trading data in real-time and load the processed data into Amazon Redshift using Amazon Kinesis Data Firehose. Use Amazon EMR to process the trading data and load the processed data into Amazon Redshift. Use Amazon Redshift Spectrum to directly analyze the trading data in the Kinesis Data Streams and join it with the data in the Redshift cluster. Use AWS Lambda to process the data from Kinesis Data Streams and load it into Amazon Redshift.