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A smart home automation company must efficiently ingest and process messages from various connected devices and sensors. The majority of these messages are comprised of a large number of small files. These messages are ingested using Amazon Kinesis Data Streams and sent to Amazon S3 using a Kinesis data stream consumer application. The Amazon S3 message data is then passed through a processing pipeline built on Amazon EMR running scheduled PySpark jobs.
The data platform team manages data processing and is concerned about the efficiency and cost of downstream data processing. They want to continue to use PySpark.
Which solution improves the efficiency of the data processing jobs and is well architected?
A marketing company is storing its campaign response data in Amazon S3. A consistent set of sources has generated the data for each campaign. The data is saved into Amazon S3 as .csv files. A business analyst will use Amazon Athena to analyze each campaign's dat
a. The company needs the cost of ongoing data analysis with Athena to be minimized.
Which combination of actions should a data analytics specialist take to meet these requirements? (Choose two.)
A technology company is creating a dashboard that will visualize and analyze time-sensitive dat
a. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support near-real-time data.
Which visualization solution will meet these requirements?
A company uses Amazon Connect to manage its contact center. The company uses Salesforce to manage its customer relationship management (CRM) dat
a. The company must build a pipeline to ingest data from Amazon Connect and Salesforce into a data lake that is built on Amazon S3.
Which solution will meet this requirement with the LEAST operational overhead?
A company is using an AWS Lambda function to run Amazon Athena queries against a cross-account AWS Glue Data Catalog. A query returns the following error:
HIVE METASTORE ERROR
The error message states that the response payload size exceeds the maximum allowed payload size. The queried table is already partitioned, and the data is stored in an
Amazon S3 bucket in the Apache Hive partition format.
Which solution will resolve this error?
