Amazon Web Services Data-Engineer-Associate - AWS Certified Data Engineer - Associate (DEA-C01)

To ensure that Branch B is up to date with the latest changes in the master branch before submitting a pull request, the correct approach is to perform a git rebase. This command rewrites the commit history so that Branch B will be based on the latest changes in the master branch.

git rebase master:

This command moves the commits of Branch B to be based on top of the latest state of the master branch. It allows the developer to resolve any conflicts and create a clean history.

Option A is correct because Amazon Redshift supports the EXCLUDE clause in the SELECT list for exactly this use case: returning all columns from a wide table except a small set of unwanted columns. AWS documentation states that “EXCLUDE column_list names the columns that are excluded from the query results” and notes that the option is helpful when only a subset of columns must be excluded from a wide table. That matches this question precisely because the table contains 100 columns and only company_id and unique_system_id need to be omitted.

Option B is invalid SQL syntax in Amazon Redshift because NOT IN is used in predicates, not in the select list to remove columns. Option C is also incorrect because EXCEPT in Redshift is a set operator used between query result sets, not a select-list column exclusion feature. AWS documents EXCEPT alongside UNION and INTERSECT as result-set comparison operators, not as a way to omit columns from SELECT *. Option D is not valid Redshift SQL syntax for column selection. Therefore, the correct and documented Redshift statement is SELECT * EXCLUDE company_id, unique_system_id FROM orders.complete_orders_history;

Apache Parquet is a columnar storage format that is much more space-efficient than row-based formats like CSV, especially for analytics workloads. Transforming data from CSV to Parquet significantly reduces storage costs and improves query performance. According to the study guide:

“Parquet is a columnar storage file format that is optimized for use with analytics workloads, providing efficient storage and fast query performance.”

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

By switching to Parquet, the company can reduce both storage size and retrieval times, making it the optimal choice for cost-effective data analysis.

Option A is correct because Amazon Redshift dynamic data masking is designed to hide, obfuscate, or partially reveal sensitive values at query time without duplicating the underlying data. AWS states that masking policies can be attached to one or more columns and can be applied differently to certain users or roles. AWS also provides examples where different roles see different versions of the same column value, which matches the requirement that customer support users see only the last four characters while audit users can see the full value.

Option B is incorrect because metadata security controls metadata visibility, not column-value masking. Option C is incorrect because row-level security filters which rows are visible; it does not selectively mask part of a column value. Option D would create a second table and therefore duplicates data, which the question forbids. Dynamic data masking is the AWS-native feature that satisfies role-based partial exposure of a single sensitive column with no data duplication.

The best solution to meet the requirements of giving data scientists the ability to query all data sources by using syntax similar to SQL with the least operational overhead is to use AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use Amazon Athena to query the data, use SQL for structured data sources, and use PartiQL for data that is stored in JSON format.

AWS Glue is a serverless data integration service that makes it easy to prepare, clean, enrich, and move data between data stores1. AWS Glue crawlers are processes that connect to a data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in the Data Catalog2. The Data Catalog is a persistent metadata store that contains table definitions, job definitions, and other control information to help you manage your AWS Glue components3. You can use AWS Glue to crawl the data sources, such as Amazon S3, Amazon RDS for Microsoft SQL Server, and Amazon DynamoDB, and store the metadata in the Data Catalog.

Amazon Athena is a serverless, interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL or Python4. Amazon Athena also supports PartiQL, a SQL-compatible query language that lets you query, insert, update, and delete data from semi-structured and nested data, such as JSON. You can use Amazon Athena to query the data from the Data Catalog using SQL for structured data sources, such as .csv files and relational databases, and PartiQL for data that is stored in JSON format. You can also use Athena to query data from other data sources, such as Amazon Redshift, using federated queries.

Using AWS Glue and Amazon Athena to query all data sources by using syntax similar to SQL is the least operational overhead solution, as you do not need to provision, manage, or scale any infrastructure, and you pay only for the resources you use. AWS Glue charges you based on the compute time and the data processed by your crawlers and ETL jobs1. Amazon Athena charges you based on the amount of data scanned by your queries. You can also reduce the cost and improve the performance of your queries by using compression, partitioning, and columnar formats for your data in Amazon S3.

Option B is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, and use Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena. Redshift Spectrum is a feature of Amazon Redshift, a fully managed data warehouse service, that allows you to query and join data across your data warehouse and your data lake using standard SQL. While Redshift Spectrum is powerful and useful for many data warehousing scenarios, it is not necessary or cost-effective for querying all data sources by using syntax similar to SQL. Redshift Spectrum charges you based on the amount of data scanned by your queries, which is similar to Amazon Athena, but it also requires you to have an Amazon Redshift cluster, which charges you based on the node type, the number of nodes, and the duration of the cluster5. These costs can add up quickly, especially if you have large volumes of data and complex queries. Moreover, using Redshift Spectrum would introduce additional latency and complexity, as you would have to provision and manage the cluster, and create an external schema and database for the data in the Data Catalog, instead of querying it directly from Amazon Athena.

Option C is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format, store the transformed data in an S3 bucket, and use Amazon Athena to query the original and transformed data from the S3 bucket, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Glue jobs are ETL scripts that you can write in Python or Scala to transform your data and load it to your target data store. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes6. While using AWS Glue jobs and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to write, run, and monitor the ETL jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using AWS Glue jobs and Parquet would introduce additional latency, as you would have to wait for the ETL jobs to finish before querying the transformed data.

Option D is not the best solution, as using AWS Lake Formation to create a data lake, use Lake Formation jobs to transform the data from all data sources to Apache Parquet format, store the transformed data in an S3 bucket, and use Amazon Athena or Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Lake Formation is a service that helps you centrally govern, secure, and globally share data for analytics and machine learning7. Lake Formation jobs are ETL jobs that you can create and run using the Lake Formation console or API. While using Lake Formation and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to create, run, and monitor the Lake Formation jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using Lake Formation and Parquet would introduce additional latency, as you would have to wait for the Lake Formation jobs to finish before querying the transformed data. Furthermore, using Redshift Spectrum to query the data would also incur the same costs and complexity as mentioned in option B. References:

What is Amazon Athena?

Data Catalog and crawlers in AWS Glue

AWS Glue Data Catalog

Columnar Storage Formats

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

AWS Glue Schema Registry

What is AWS Glue?

Amazon Redshift Serverless

Amazon Redshift provisioned clusters

[Querying external data using Amazon Redshift Spectrum]

[Using stored procedures in Amazon Redshift]

[What is AWS Lambda?]

[PartiQL for Amazon Athena]

[Federated queries in Amazon Athena]

[Amazon Athena pricing]

[Top 10 performance tuning tips for Amazon Athena]

[AWS Glue ETL jobs]

[AWS Lake Formation jobs]

To achieve the highest throughput and efficiently use cluster resources while loading data into an Amazon Redshift cluster, the optimal approach is to use a single COPY command that ingests data in parallel.

Option D: Use a single COPY command to load the data into the Redshift cluster.The COPY command is designed to load data from multiple files in parallel into a Redshift table, using all the cluster nodes to optimize the load process. Redshift is optimized for parallel processing, and a single COPY command can load multiple files at once, maximizing throughput.

Options A, B, and C either involve unnecessary complexity or inefficient approaches, such as using multiple COPY commands or INSERT statements, which are not optimized for bulk loading.

Amazon Kinesis Data Firehose is a fully managed data ingestion service that enables reliable and scalable delivery of streaming and batch data into Amazon S3 with minimal operational overhead. This directly satisfies the requirement for a fully managed AWS ingestion service while avoiding the need to provision, scale, or manage infrastructure.

By using the Amazon Kinesis Agent on the on-premises servers, the company can automatically forward daily data files to Kinesis Data Firehose. Firehose handles buffering, retry logic, scaling, and delivery without requiring administrative effort. Delivering the data to Amazon S3 allows seamless integration with Amazon Athena, which natively queries data stored in S3 without requiring data movement or transformation.

Enabling Amazon S3 versioning protects files from accidental deletion by preserving previous versions of objects. This aligns with AWS best practices for data durability and governance, especially for analytics workloads and compliance requirements.

Other options introduce unnecessary operational complexity. AWS DataSync with Amazon EFS is not optimized for Athena-based analytics. AWS Glue jobs and Amazon RDS are unsuitable for file-based analytical access. A self-managed Apache Kafka solution with Amazon MSK significantly increases operational overhead.

Therefore, option B is the most efficient, scalable, and operationally optimal solution according to AWS Certified Data Engineer – Associate best practices.

 Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3 and Amazon Redshift. Amazon AppFlow supports many SaaS applications as data sources and targets, and allows you to configure data flows with a few clicks. Amazon AppFlow also provides features such as data transformation, filtering, validation, and encryption to prepare and protect your data. Amazon AppFlow meets the requirements of the media company with the least operational overhead, as it eliminates the need to write code, manage infrastructure, or monitor data pipelines. References:

Amazon AppFlow

Amazon AppFlow | SaaS Integrations List

Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions