SAA-C03 Questions Bank

The solution that will meet the requirements is to create Amazon Kinesis data streams for data ingestion, create Amazon Kinesis Data Firehose delivery streams to consume the Kinesis data streams, and specify the S3 bucket as the destination of the delivery streams. This solution will allow the company’s application to ingest real-time data from third-party applications and place the ingested raw data in an S3 bucket. Amazon Kinesis data streams are scalable and durable streams that can capture and store data from hundreds of thousands of sources. Amazon Kinesis Data Firehose is a fully managed service that can deliver streaming data to destinations such as S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk. Amazon Kinesis Data Firehose can also transform and compress the data before delivering it to S3.

The other solutions are not as effective as the first one because they either do not support real-time data ingestion, do not work with third-party applications, or do not use S3 as the destination. Creating database migration tasks in AWS Database Migration Service (AWS DMS) will not support real-time data ingestion, as AWS DMS is mainly designed for migrating relational databases, not streaming data. AWS DMS also requires replication instances, source endpoints, and target endpoints to be compatible with specific database engines and versions. Creating and configuring AWS DataSync agents on the EC2 instances will not work with third-party applications, as AWS DataSync is a service that transfers data between on-premises storage systems and AWS storage services, not between applications. AWS DataSync also requires installing agents on the source or destination servers. Creating an AWS Direct Connect connection to the application for data ingestion will not use S3 as the destination, as AWS Direct Connect is a service that establishes a dedicated network connection between on-premises andAWS, not between applications and storage services. AWS Direct Connect also requires a physical connection to an AWS Direct Connect location.

This solution provides the appropriate user access most cost-effectively because it uses the Amazon S3 Intelligent-Tiering storage class, which automatically optimizes storage costs by moving data to the most cost-effective access tier when access patterns change, without performance impact or operational overhead1. This storage class is ideal for datawith unknown, changing, or unpredictable access patterns, such as photos that are heavily viewed for months or less than a week. By storing the photo metadata and its S3 location in DynamoDB, the application can quickly query and retrieve the relevant photos for each user. DynamoDB is a fast, scalable, and fully managed NoSQL database service that supports key-value and document data models2.

This solution meets the requirements because it allows the company to use both predictive scaling and dynamic scaling to optimize the capacity of its Auto Scaling group. Predictive scaling uses machine learning to analyze historical data and forecast future traffic patterns. It then adjusts the desired capacity of the group in advance of the predicted changes. Dynamic scaling uses target tracking to maintain a specified metric (such as CPU utilization) at a target value. It scales the group in or out as needed to keep the metric close to the target. By using both scaling methods, the company can benefitfrom faster, simpler, and more accurate scaling that responds to both forecasted and live changes in utilization.

AWS Glue is a serverless data integration service that can crawl, catalog, and prepare data for analysis. AWS Glue can automatically discover the schema and partitioning of the data stored in Apache Parquet format in S3, and create a table in the AWS Glue Data Catalog. Amazon Athena is a serverless interactive query service that can run SQL queries directly on data in S3, without requiring any data loading or transformation. Athena can use the table metadata from the AWS Glue Data Catalog to query the data in S3. By using AWS Glue and Athena, you can analyze the log files in S3 most cost-effectively, as you only pay for the resources consumed by the crawler and the queries, and you do not need to provision or manage any servers or clusters.