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

AWS Glue streaming jobs are purpose-built for low-latency, near real-time ETL pipelines.

Glue Studio allows visual job authoring and supports complex transformations, including joins with Amazon RDS databases, through JDBC connectors.

Data can be written directly to S3 in Apache Parquet format using built-in AWS Glue support.

Options B and C are more suitable for batch or development/testing. Option D is for custom batch jobs, not real-time processing.

“You can use AWS Glue streaming jobs with Glue Studio to process streaming data, perform transformations, join with RDS data sources, and write results in formats like Parquet to S3.”

The best solution for managing SSL/TLS certificates on EC2 instances with minimal operational overhead is to use AWS Certificate Manager (ACM). ACM simplifies certificate management by automating the provisioning, renewal, and deployment of certificates.

AWS Certificate Manager (ACM):

ACM manages SSL/TLS certificates for EC2 and other AWS resources, including automatic certificate renewal. This reduces the need for manual management and avoids operational complexity.

ACM also integrates with other AWS services to simplify secure connections between AWS infrastructure and customer-managed environments.

The best solution for fast, event-driven processing of unpredictable file uploads is to use S3 event notifications, CloudTrail, and EventBridge to automatically trigger the AWS Glue workflow:

“You can configure S3 PutObject events to be captured by CloudTrail and forwarded through EventBridge to trigger an AWS Glue job or workflow. This allows Glue to begin processing as soon as the file arrives, with minimal latency.”

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

This option provides the lowest latency and least manual overhead compared to polling or scheduling solutions.

AWS Glue DataBrew provides a no-code data preparation and validation interface. It allows you to set data profiling, completeness checks, and numeric range validations directly through its UI—ideal for IoT validation use cases.

“AWS Glue DataBrew enables users to define validation rules such as completeness and value range checks without writing code.”

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

This fulfills the requirement for minimal coding and automatic data quality flagging.

Option C (LAC-A) is the correct choice because the requirement is to always show the Region-level total even when the visual is drilled down into lower levels (for example, country → city → store). A simple calculated field (Option B) is computed at the row level and then aggregated by the visual, so it will change as the drill-down changes the grain. A table calculation (Option A) is evaluated based on the current visual layout and can vary with the fields placed in the visual, which makes it unreliable for enforcing a fixed “Region total regardless of drill-down.”

A level-aware calculation (aggregate) is specifically intended to “lock” an aggregation to a chosen dimensional level (here: Region). That means you can compute revenue aggregated at the Region level and reuse that value across lower drill levels without it recalculating at city/store granularity. A window LAC (LAC-W) is primarily for windowed analytics (running totals, period-over-period, rank, moving averages) over a partition/order, not for enforcing a fixed dimensional aggregation level. Therefore, LAC-A best matches the requirement.

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.

 Changing the distribution key to the table column that has the largest dimension will help to balance the load more evenly across all five compute nodes. The distribution key determines how the rows of a table are distributed among the slices of the cluster. If the distribution key is not chosen wisely, it can cause data skew, meaning some slices will have more data than others, resulting in uneven CPU load and query performance. By choosing the table column that has the largest dimension, meaning the column that has the most distinct values, as the distribution key, the data engineer can ensure that the rows are distributed more uniformly across the slices, reducing data skew and improving query performance.

The other options are not solutions that will meet the requirements. Option A, changing the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load. The sort key determines the order in which the rows of a table are stored on disk, which can improve the performance of range-restricted queries, but not the load balancing. Option C, upgrading the reserved node from ra3.4xlarge to ra3.16xlarge, will not maintain the current number of compute nodes, as it will increase the cost and the capacity of the cluster. Option D, changing the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load either. The primary key is a constraint that enforces the uniqueness of the rows in a table, but it does not influence the data layout or the query optimization. References:

Choosing a data distribution style

Choosing a data sort key

Working with primary keys

 The error message indicates that the AWS Glue job cannot access the Amazon S3 bucket through the VPC endpoint. This could be because the VPC’s route table does not have the necessary routes to direct the traffic to the endpoint. To fix this, the data engineer must verify that the route table has an entry for the Amazon S3 service prefix (com.amazonaws.region.s3) with the target as the VPC endpoint ID. This will allow the AWS Glue job to use the VPC endpoint to access the S3 bucket without going through the internet or a NAT gateway. For more information, see Gateway endpoints. References:

Troubleshoot the AWS Glue error “VPC S3 endpoint validation failed”

Amazon VPC endpoints for Amazon S3

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

Option C is correct because AWS Lambda’s advanced logging controls support separate application log level and system log level filtering, but AWS documentation states that for Lambda to filter application logs according to their log level, the function must use JSON formatted logs. AWS also documents that in the advanced logging configuration you can choose a log level such as ERROR for application logs and WARN for system logs. Therefore, configuring the function to use JSON log format is the necessary step that enables the required log-level filtering behavior.

Option A is irrelevant because the function already writes logs successfully. Option B alone is insufficient because changing code-level logging does not configure Lambda’s separate system log filtering behavior, and Lambda’s application log filtering relies on structured JSON logs. Option D changes the log destination but does not implement the required filtering levels. The official Lambda documentation makes clear that JSON log format is the enabling configuration for this feature, so that is the correct answer.