Data-Engineer-Associate Amazon Web Services AWS Certified Data Engineer - Associate (DEA-C01) Free Practice Exam Questions (2026 Updated)

Amazon Athena is a serverless, interactive query service that enables you to analyze data in Amazon S3 using standard SQL. AWS Lake Formation is a service that helps you build, secure, and manage data lakes on AWS. You can use AWS Lake Formation to create data filters that define the level of access for different IAM roles based on the columns, rows, or tags of the data. By using Amazon Athena to query the data and AWS Lake Formation to create data filters, the company can meet the requirements of ensuring that user groups can access only the PII that they require with the least effort. The solution is to use Amazon Athena to query the data in the data lake that is in Amazon S3. Then, set up AWS Lake Formation and create data filters to establish levels of access for the company’s IAM roles. For example, a data filter can allow a user group to access only the columns that contain the PII that they need, such as name and email address, and deny access to the columns that contain the PII that they do not need, such as phone number and social security number. Finally, assign each user to the IAM role that matches the user’s PII access requirements. This way, the user groups can access the data in the data lake securely and efficiently. The other options are either not feasible or not optimal. Using Amazon QuickSight to access the data (option B) would require the company to pay for the QuickSight service and to configure the column-level security features for each user. Building a custom query builder UI that will run Athena queries in the background to access the data (option C) would require the company to develop and maintain the UI and to integrate it with Amazon Cognito. Creating IAM roles that have different levels of granular access (option D) would require the company to manage multiple IAM roles and policies and to ensure that they are aligned with the data schema. References:

Amazon Athena

AWS Lake Formation

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.3: Amazon Athena

The issue described is that the AWS Glue job is reprocessing files from previous runs despite the bookmark feature being enabled. Bookmarks in AWS Glue allow jobs to keep track of which files or data have already been processed to avoid reprocessing. The most likely reason for reprocessing the files is missing S3 permissions, specifically s3

s3

is a permission required by AWS Glue when bookmarks are enabled to ensure Glue can retrieve metadata from the files in S3, which is necessary for the bookmark mechanism to function correctly. Without this permission, Glue cannot track which files have been processed, resulting in reprocessing during subsequent runs.

Concurrency settings (Option B) and the version of AWS Glue (Option C) do not affect the bookmark behavior. Similarly, the lack of a commit statement (Option D) is not applicable in this context, as Glue handles commits internally when interacting with Redshift and S3.

Thus, the root cause is likely related to insufficient permissions on the S3 bucket, specifically s3

, which is required for bookmarks to work as expected.

AWS Glue DataBrew is purpose-built for data profiling and data preparation with minimal operational overhead. A DataBrew profile job automatically analyzes datasets stored in Amazon S3 and generates detailed statistics such as column completeness, distinct values, data types, min/max values, and potential data quality issues. This is exactly what is required before data preparation steps.

The dataset size (100 MB) and Parquet format are fully supported by DataBrew, and no infrastructure provisioning, cluster management, or SQL development is required. The profiling results are produced automatically and can be reviewed directly in the AWS Management Console, making this the most efficient and lowest-effort solution.

Amazon Managed Service for Apache Flink is designed for real-time streaming analytics and introduces unnecessary complexity. Amazon Redshift Spectrum requires schema definition, SQL development, and additional setup, which is excessive for simple profiling. Amazon QuickSight is a visualization and BI service, not a data profiling tool.

Therefore, creating a DataBrew profile job is the most operationally efficient and exam-aligned solution.

Amazon Athena Federated Query is a feature that allows you to query data from multiple sources using standard SQL. You can use Athena Federated Query to join data from Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3, as well as other data sources such as MongoDB, Apache HBase, and Apache Kafka1. Athena Federated Query is a serverless and interactive service, meaning you do not need to provision or manage any infrastructure, and you only pay for the amount of data scanned by your queries. Athena Federated Query is the most cost-effective solution for performing a one-time analysis job on data from multiple sources, as it eliminates the need to copy or move data, and allows you to query data directly from the source.

The other options are not as cost-effective as Athena Federated Query, as they involve additional steps or costs. Option A requires you to provision and pay for an Amazon EMR cluster, which can be expensive and time-consuming for a one-time job. Option B requires you to copy or move data from DynamoDB, RDS, and Redshift to S3, which can incur additional costs for data transfer and storage, and also introduce latency and complexity. Option D requires you to have an existing Redshift cluster, which can be costly and may not be necessary for a one-time job. Option D also does not support querying data from RDS directly, so you would need to use Redshift Federated Query to access RDS data, which adds another layer of complexity2. References:

Amazon Athena Federated Query

Redshift Spectrum vs Federated Query

AWS security best practices require avoiding long-term credentials and enforcing the principle of least privilege. Using IAM roles with role assumption is the most secure approach for granting temporary, scoped access to AWS resources.

In this solution, an IAM role is created with permissions to access the Amazon RDS databases. A second IAM role is assigned to the AWS Lambda functions and AWS Glue jobs with permission to assume the RDS-access role. This approach eliminates hard-coded credentials, enables automatic credential rotation through AWS Security Token Service (AWS STS), and provides clear separation of duties.

Using the root user or long-term access keys is explicitly discouraged by AWS. Creating IAM users for services violates best practices, as AWS services should use IAM roles, not users. Embedding credentials in JDBC connection strings exposes sensitive information and increases the risk of credential leakage.

Therefore, using IAM role assumption provides the strongest security posture, auditability, and compliance with AWS Well-Architected Framework guidance.

Option B is the most operationally efficient because it checks the business requirement directly: whether the target table contains the most recent data, not merely whether a job failed. Monitoring only failures (Options C and D) can produce false positives (a job failure might not impact freshness) and false negatives (a job can succeed but still load stale or incomplete data). The study material emphasizes implementing data quality validation as part of the ETL process so data can be verified before or as it is stored, rather than relying only on pipeline execution status.

Using a data quality rule focused on freshness (for example, validating that a “max event timestamp” or “latest partition date” meets today’s expected value) lets the pipeline detect stale loads even when the workflow runs. Then, an EventBridge rule can route failures of that data quality check to SNS for immediate notification, keeping operations serverless and centralized. Macie (Option A) is designed for sensitive-data discovery/classification, not operational “freshness” checks on Redshift tables, so it adds unnecessary services and effort compared to a Glue-native data quality validation approach.

AWS Glue Data Catalog stores metadata about data stored in Amazon S3, including table schemas and partitions. If new partitions are added to S3 and the Glue Data Catalog is not updated—either through crawlers or explicit API calls—query engines such as Amazon Athena and AWS Glue will not be aware of the new data.

This leads to analysis results that appear incomplete or outdated, even though the underlying data exists in S3. This is a common issue in time-sensitive analytics workloads where data arrives frequently and partitions change often.

IAM permission issues would typically cause access failures rather than missing records. S3 versioning affects object history, not query visibility. Overlapping Glue job schedules could cause processing delays but would not directly cause missing data during query time.

Therefore, the most likely cause is that the Glue Data Catalog metadata is not synchronized with the latest S3 partitions.