Data-Engineer-Associate Exam Dumps : AWS Certified Data Engineer - Associate (DEA-C01)

When AWS Glue jobs run inside a private subnet, they must use secure and supported methods to access external databases. AWS Glue supports JDBC connections to on-premises databases, but best practices require secure credential management and explicit driver configuration.

Using a JDBC driver stored in Amazon S3 allows Glue to connect to PostgreSQL without relying on default drivers. Storing credentials in AWS Secrets Manager eliminates hard-coded credentials and enables secure rotation, aligning with AWS security best practices.

Simply specifying credentials inline is less secure and not recommended. SASL connections are not supported for PostgreSQL JDBC connections. Security groups alone do not establish connectivity or authentication.

Therefore, Option D is the correct and production-grade solution.

Problem Analysis:

The company runs a daily AWS Glue ETL pipeline to clean and transform files received in an S3 bucket.

If a file is incomplete or empty, the previous day’s file should be retained.

Need a solution to validate files before overwriting the existing file.

Key Considerations:

Automate data validation with minimal human intervention.

Use built-in AWS Glue capabilities for ease of integration.

Ensure robust validation for missing or incomplete data.

Solution Analysis:

Option A: Lambda Function for Validation

Lambda can validate files, but it would require custom code.

Does not leverage AWS Glue’s built-in features, adding operational complexity.

Option B: AWS Glue Data Quality Rules

AWS Glue Data Quality allows defining Data Quality Definition Language (DQDL) rules.

Rules can validate if required fields are missing or if the file is empty.

Automatically integrates into the existing ETL pipeline.

If validation fails, retain the previous day’s file.

Option C: AWS Glue Studio with Filling Missing Values

Modifying ETL code to fill missing values with most common values risks introducing inaccuracies.

Does not handle empty files effectively.

Option D: Athena Query for Validation

Athena can drop rows with missing values, but this is a post-hoc solution.

Requires manual intervention to copy the corrected file to S3, increasing complexity.

Final Recommendation:

Use AWS Glue Data Quality to define validation rules in DQDL for identifying missing or incomplete data.

This solution integrates seamlessly with the ETL pipeline and minimizes manual effort.

Implementation Steps:

Enable AWS Glue Data Quality in the existing ETL pipeline.

Define DQDL Rules, such as:

Check if a file is empty.

Verify required fields are present and non-null.

Configure the pipeline to proceed with overwriting only if the file passes validation.

In case of failure, retain the previous day’s file.

AWS Glue Data Quality Overview

Defining DQDL Rules

AWS Glue Studio Documentation

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.