Pass the Amazon Web Services AWS Certified Data Engineer Data-Engineer-Associate Questions and answers with Dumpstech

The company wants to ensure that no customer records are deleted or modified for 7 years, and even the root user should not have the ability to change the data. S3 Object Lock in Compliance Mode is the correct solution for this scenario.

Option B: Enable compliance mode on the S3 bucket. Use a default retention period of 7 years.In Compliance Mode, even the root user cannot delete or modify locked objects during the retention period. This ensures that the data is protected for the entire 7-year duration as required. Compliance mode is stricter than governance mode and prevents all forms of alteration, even by privileged users.

Option A (Governance Mode) still allows certain privileged users (like the root user) to bypass the lock, which does not meet the company's requirement. Option C (legal hold) and Option D (setting retention per object) do not fully address the requirement to block root user modifications.

The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog that provides a central metadata repository for various data sources and formats. You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMR and Amazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWS Glue crawlers to automatically discover and catalog the metadata from Hive, and use the AWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as the Hive metastore. The other options are either more complex or require additional steps, such as setting up Apache Ranger for security, managing a Hive metastore on an EMR cluster or an RDS instance, or migrating the metadata manually. References:

Using the AWS Glue Data Catalog as the metastore for Hive (Section: Specifying AWS Glue Data Catalog as the metastore)

Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue Data Catalog)

AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadata from an existing Hive metastore)

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 131)

Amazon SageMaker ML Lineage Tracking provides a unified mechanism to automatically capture and track the lineage of ML artifacts (datasets, jobs, and models). It supports auditability, reproducibility, and compliance by maintaining metadata for every stage of the ML lifecycle.

“Use Amazon SageMaker Lineage Tracking to automatically record and track the lineage of data, training jobs, models, and endpoints to ensure auditability and reproducibility.”

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

A “No space left on device” error typically results from data skew or large shuffle stages. The best actions are:

B. Monitor using Spark UI and Glue metrics to find skewed partitions or executor issues.

D. Use --write-shuffle-files-to-s3 to offload intermediate data to S3 instead of local disk, and apply salting to reduce skew.

“You can reduce the impact of data skew and large shuffle operations by monitoring with Spark UI and enabling the --write-shuffle-files-to-s3 option. Salting can help rebalance the skewed keys.”

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

Scaling out workers (A, C) is more costly and less efficient if the root cause (skew) is not fixed.

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

AWS Database Migration Service (AWS DMS) is purpose-built to migrate and continuously replicate data from both AWS-hosted and on-premises databases. It supports full-load (existing data) and change data capture (CDC) for ongoing changes, making it the most cost-effective and operationally simple solution in this scenario.

“DMS supports both full-load and continuous replication via CDC. This enables replicating existing and future data from various sources to a data lake in Amazon S3.”

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

AWS Glue is not suitable for real-time CDC replication across hybrid environments.

 AWS Glue is a fully managed serverless ETL service that can handle petabytes of data in seconds. AWS Glue can run Apache Spark and Apache Flink jobs without requiring any infrastructure provisioning or management. AWS Glue can also integrate with Apache Pig, Apache Oozie, and Apache Hbase using AWS Glue Data Catalog and AWS Glue workflows. AWS Glue can reduce the overall operational overhead by automating the data discovery, data preparation, and data loading processes. AWS Glue can also optimize the cost and performance of ETL jobs by using AWS Glue Job Bookmarking, AWS Glue Crawlers, and AWS Glue Schema Registry. References:

AWS Glue

AWS Glue Data Catalog

AWS Glue Workflows

[AWS Glue Job Bookmarking]

[AWS Glue Crawlers]

[AWS Glue Schema Registry]

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

When processing many small files, the overhead of managing partitions can degrade performance. Rather than scaling workers manually, enabling AWS Glue auto scaling dynamically adjusts worker count based on resource requirements, which is cost-effective and addresses the performance concern.

“AWS Glue auto scaling automatically adds or removes workers based on the workload, which is efficient for handling performance bottlenecks when working with many small files.”

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

Manually scaling up workers or increasing their size (A, B) can be more expensive and less adaptive.

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.