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

Athena workgroups are a way to isolate query execution and query history among users, teams, and applications that share the same AWS account. By creating a workgroup for each use case, the company can control the access and actions on the workgroup resource using resource-level IAM permissions or identity-based IAM policies. The company can also use tags to organize and identify the workgroups, and use them as conditions in the IAM policies to grant or deny permissions to the workgroup. This solution meets the requirements of separating query processes and access to query history among users, teams, and applications that are in the same AWS account. References:

Athena Workgroups

IAM policies for accessing workgroups

Workgroup example policies

Amazon Athena charges per amount of data scanned by each query. When the same query with identical parameters is executed multiple times, re-scanning the data repeatedly increases both cost and execution time. Athena provides query result reuse, which allows previously computed query results to be reused when an identical query is rerun within a configurable time window.

By enabling query result reuse at the Athena workgroup level, Athena automatically returns cached results instead of re-reading data from Amazon S3. This significantly improves query performance while reducing data scan costs. This feature is specifically designed for workloads where analysts frequently rerun complex queries with the same parameters, which directly matches the scenario.

Converting data to JSON would degrade performance because JSON is not a columnar format and increases scan size. Using Amazon EMR introduces unnecessary infrastructure management and cost for a query optimization problem that Athena already solves natively. Amazon Redshift Spectrum is intended for querying S3 data from within Redshift and does not address repeated-query optimization in Athena.

Therefore, configuring query result reuse in the Athena workgroup is the most cost-effective and operationally efficient solution.

To query the Sales table in Amazon Redshift for city names that start with " San " or " El, " the appropriate query uses a regular expression (regex) pattern to match city names that begin with those prefixes.

Option B: Select * from Sales where city_name ~ ' ^(San|El) ' ;In Amazon Redshift, the ~ operator is used to perform pattern matching using regular expressions. The ^(San|El) pattern matches city names that start with " San " or " El. " This is the correct SQL syntax for this use case.

Other options (A, C, D) contain incorrect syntax or incorrect use of special characters, making them invalid queries.

Amazon Redshift query performance for large joins depends heavily on data distribution and query optimization. Using the KEY distribution style on both tables ensures that rows with the same join key are co-located on the same compute nodes, minimizing costly data redistribution during joins.

Selecting a high-cardinality column as the distribution key is critical to evenly distribute data across nodes and prevent data skew. Low-cardinality keys can cause hotspots where too much data resides on a small number of nodes, degrading performance.

Using EVEN distribution on the 10-billion-row table and ALL distribution on the 900-million-row table is inefficient because ALL distribution replicates the entire table to every node, which is not appropriate for large datasets. The Amazon Redshift query optimizer operates automatically and does not require manual selection.

Amazon Redshift Advisor analyzes workload patterns and provides actionable recommendations for distribution styles, sort keys, and query optimizations. Using Redshift Advisor allows the data engineer to apply proven optimizations based on actual cluster usage.

Therefore, combining KEY distribution with a high-cardinality join column and leveraging Amazon Redshift Advisor recommendations is the most effective solution to improve query performance.

Scheduling an AWS Glue crawler to periodically update the Data Catalog automates the process of detecting new partitions and updating the catalog, which minimizes manual maintenance and operational overhead.

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;

Option D is correct because domain units in Amazon SageMaker Unified Studio are specifically designed to organize assets and other domain entities by business unit or team, while also enabling delegated authority to domain unit owners for authorization and governance. AWS states that domain units let selected users within each business unit log in and share assets to the catalog, and that users elsewhere in the enterprise can discover those assets and request access. This directly matches the requirement for isolated control with controlled cross-business-unit sharing upon approval. AWS also documents that Unified Studio supports authentication through AWS IAM Identity Center, which satisfies the need for centralized authentication and identity mapping.

Option A is incorrect because API keys are not the correct authentication model for Unified Studio portal access; AWS documents IAM Identity Center and related identity-provider-based sign-in instead. Option B is wrong because it prevents the required approved sharing. Option C is less suitable because the requirement is to isolate business units within a domain while still enabling governed sharing; separate domains would add more separation than needed and complicate sharing. Therefore, one domain with separate domain units, IAM Identity Center, and access-request-based sharing is the best fit.

Option B is the only solution that supports near real-time updates from a continuously changing source to Amazon Redshift. The requirement says the on-premises PostgreSQL database is “continuously updated” and the target must be updated “as quickly as possible.” Nightly full backups or nightly full loads (Options A and D) inherently introduce at least a daily lag, which violates the freshness requirement. Similarly, exporting with pg_dump and reloading with COPY (Option C) is a batch approach and does not provide continuous change propagation.

The study material explicitly positions AWS Database Migration Service (DMS) for database migrations and highlights that it supports both full-load and change data capture (CDC), and that CDC enables continuous replication so ongoing changes can be applied after the initial load.

Therefore, a DMS task configured for full load + CDC provides the fastest ongoing synchronization pattern: it performs the initial migration and then continuously captures and applies changes so Redshift stays current with minimal delay compared to periodic batch reloads.