AWS Certified Associate Data-Engineer-Associate Full Course Free

 Option C is the best solution to meet the requirements with the least effort because server-side encryption with AWS KMS keys (SSE-KMS) is a feature that allows you to encrypt data at rest in Amazon S3 using keys managed by AWS Key Management Service (AWS KMS). AWS KMS is a fully managed service that enables you to create and manage encryption keys for your AWS services and applications. AWS KMS also allows you to define granular access policies for your keys, such as who can use them to encrypt and decrypt data, and under what conditions. By using SSE-KMS, you can protect your S3 objects by using encryption keys that only specific employees can access, without having to manage the encryption and decryption process yourself.

Option A is not a good solution because it involves using AWS CloudHSM, which is a service that provides hardware security modules (HSMs) in the AWS Cloud. AWS CloudHSM allows you to generate and use your own encryption keys on dedicated hardware that is compliant with various standards and regulations. However, AWS CloudHSM is not a fully managed service and requires more effort to set up and maintain than AWS KMS. Moreover, AWS CloudHSM does not integrate with Amazon S3, so you have to configure the process that writes to S3 to make calls to CloudHSM to encrypt and decrypt the objects, which adds complexity and latency to the data protection process.

Option B is not a good solution because it involves using server-side encryption with customer-provided keys (SSE-C), which is a feature that allows you to encrypt data at rest in Amazon S3 using keys that you provide and manage yourself. SSE-C requires you to send your encryption key along with each request to upload or retrieve an object. However, SSE-C does not provide any mechanism to restrict access to the keys that encrypt the objects, so you have to implement your own key management and access control system, which adds more effort and risk to the data protection process.

Option D is not a good solution because it involves using server-side encryption with Amazon S3 managed keys (SSE-S3), which is a feature that allows you to encrypt data at rest in Amazon S3 using keys that are managed by Amazon S3. SSE-S3 automatically encrypts and decrypts your objects as they are uploaded and downloaded from S3. However, SSE-S3 does not allow you to control who can access the encryption keys or under what conditions. SSE-S3 uses a single encryption key for each S3 bucket, which is shared by all users who have access to the bucket. This means that you cannot restrict access to the keys that encrypt the objects by specific employees, which does not meet the requirements.

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Protecting Data Using Server-Side Encryption with AWS KMS–Managed Encryption Keys (SSE-KMS) - Amazon Simple Storage Service

What is AWS Key Management Service? - AWS Key Management Service

What is AWS CloudHSM? - AWS CloudHSM

Protecting Data Using Server-Side Encryption with Customer-Provided Encryption Keys (SSE-C) - Amazon Simple Storage Service

Protecting Data Using Server-Side Encryption with Amazon S3-Managed Encryption Keys (SSE-S3) - Amazon Simple Storage Service

For query workloads on S3 data that depend on date-based filters, partitioning by order date optimizes performance and cost because Athena reads only the relevant partitions.

Athena scales automatically and doesn’t degrade with increasing data size when partitions are managed efficiently.

“Partitioning data in Amazon S3 based on query predicates such as order date improves Athena query performance and reduces scanned data volume.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This is the most cost-effective and scalable option for date-based queries.

Option B is the best solution to meet the requirements with the least operational overhead because S3 Select is a feature that allows you to retrieve only a subset of data from an S3 object by using simple SQL expressions. S3 Select works on objects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid the need to download and process the entire S3 object, which reduces the amount of data transferred and the computation time. S3 Select is also easy to use and does not require any additional services or resources.

Option A is not a good solution because it involves writing custom code and configuring an AWS Lambda function to load data from the S3 bucket into a pandas dataframe and query the required column. This option adds complexity and latency to the data retrieval process and requires additional resources and configuration. Moreover, AWS Lambda has limitations on the execution time, memory, and concurrency, which may affect the performance and reliability of the data retrieval process.

Option C is not a good solution because it involves creating and running an AWS Glue DataBrew project to consume the S3 objects and query the required column. AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. However, in this scenario, the data is already in Parquet format, which is a columnar storage format that is optimized for analytics. Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover, AWS Glue DataBrew adds extra time and cost to the data retrieval process and requires additional resources and configuration.

Option D is not a good solution because it involves running an AWS Glue crawler on the S3 objects and using a SQL SELECT statement in Amazon Athena to query the required column. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. However, in this scenario, the schema and format of the data are already known and fixed, so there is no need to run a crawler to discover them. Moreover, running a crawler and using Amazon Athena adds extra time and cost to the data retrieval process and requires additional services and configuration.

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

S3 Select and Glacier Select - Amazon Simple Storage Service

AWS Lambda - FAQs

What Is AWS Glue DataBrew? - AWS Glue DataBrew

Populating the AWS Glue Data Catalog - AWS Glue

What is Amazon Athena? - Amazon Athena

The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. References:

Using the Amazon Redshift Data API

Using Amazon EventBridge with Amazon Redshift

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift