Data-Engineer-Associate Exam Dumps - Amazon Web Services AWS Certified Data Engineer Questions and Answers

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.

Problem Analysis:

The company uses AWS Glue for ETL pipelines and must enforce data quality checks during pipeline execution.

The goal is to implement quality checks with minimal implementation effort.

Key Considerations:

AWS Glue provides an Evaluate Data Quality transform that allows for defining quality checks directly in the pipeline.

DQDL (Data Quality Definition Language) simplifies the process by allowing declarative rule definitions.

Solution Analysis:

Option A: SQL Transform

SQL queries can implement rules but require manual effort for each rule and do not integrate natively with Glue.

Option B: Evaluate Data Quality Transform + DQDL

AWS Glue’s built-in Evaluate Data Quality transform is designed for this use case.

Allows defining thresholds and rules in DQDL with minimal coding effort.

Option C: Custom Transform with PyDeequ

PyDeequ is a powerful library but adds unnecessary complexity compared to Glue’s native features.

Option D: Custom Transform with Great Expectations

Similar to PyDeequ, Great Expectations adds operational complexity and external dependencies.

Final Recommendation:

Use the Evaluate Data Quality transform with DQDL to implement data quality rules in AWS Glue pipelines.

AWS Glue Data Quality

DQDL Syntax and Examples

AWS Glue Studio Documentation

AWS Glue ETL is designed for scalable and serverless data processing, and it supports integrated quality enforcement using AWS Glue Data Quality, which makes it the most cost-effective and integrated option when combined with Amazon RDS for MySQL as the relational database.

“AWS Glue can perform data validation as part of the ETL process, ensuring data quality before storing the data in the target data store.”

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

Using AWS Glue Data Quality directly in the ETL workflow is simpler and more cost-effective than separating transformation (Glue) and validation (DataBrew) into different services.

Publishing AWS Glue data quality scores to Amazon DataZone requires creating a DQDL ruleset, scheduling it to run regularly, and then linking the corresponding AWS Glue table as a data source in the DataZone project. The setup ensures that data quality scores from Glue are correctly published and accessible within Amazon DataZone:

“You can define DQDL rulesets for Glue tables and publish the data quality results to DataZone when the project is configured with an AWS Glue data source and the rulesets are scheduled.”

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

Option C follows the expected flow without unnecessary complexity and aligns perfectly with the integration flow supported by AWS.

The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. References:

Using the Amazon Redshift Data API

Using Amazon EventBridge with Amazon Redshift

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift

 AWS Glue is a fully managed serverless ETL service that can handle data deduplication with minimal operational overhead. AWS Glue provides a built-in ML transform called FindMatches, which can automatically identify and group similar records in a dataset. FindMatches can also generate a primary key for each group of records and remove duplicates. FindMatches does not require any coding or prior ML experience, as it can learn from a sample of labeled data provided by the user. FindMatches can also scale to handle large datasets and optimize the cost and performance of the ETL job. References:

AWS Glue

FindMatches ML Transform

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

For processing the incoming car listings in a cost-effective, scalable, and automated way, the ideal approach involves using AWS Glue for data processing, AWS Lambda with S3 Event Notifications for orchestration, Amazon Athena for one-time queries and analytical reporting, and Amazon QuickSight for visualization on the dashboard. Let’s break this down:

AWS Glue: This is a fully managed ETL (Extract, Transform, Load) service that automatically processes the incoming data files. Glue is serverless and supports diverse data sources, including Amazon S3 and Redshift.

AWS Lambda and S3 Event Notifications: Using Lambda and S3 Event Notifications allows near real-time triggering of processing workflows as soon as new data is uploaded into S3. This approach is event-driven, ensuring that the listings are processed as soon as they are uploaded, reducing the latency for data processing.

Amazon Athena: A serverless, pay-per-query service that allows interactive queries directly against data in S3 using standard SQL. It is ideal for the requirement of one-time queries and analytical reporting without the need for provisioning or managing servers.

Amazon QuickSight: A business intelligence tool that integrates with a wide range of AWS data sources, including Athena, and is used for creating interactive dashboards. It scales well and provides real-time insights for the car listings.

This solution (Option D) is the most cost-effective, because both Glue and Athena are serverless and priced based on usage, reducing costs when compared to provisioning EMR clusters in the other options. Moreover, using Lambda for orchestration is more cost-effective than AWS Step Functions due to its lightweight nature.

The most cost-effective solution in this case is to apply a lifecycle policy to transition records to Amazon S3 Standard-IA storage after 30 days. Here’s why:

Amazon S3 Lifecycle Policies: Amazon S3 offers lifecycle policies that allow you to automatically transition objects between different storage classes to optimize costs. For data that is frequently accessed in the first 30 days and infrequently accessed after that, transitioning from the S3 Standard storage class to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days makes the most sense. S3 Standard-IA is designed for data that is accessed less frequently but still needs to be retained, offering lower storage costs than S3 Standard with a retrieval cost for access.

Cost Optimization: S3 Standard-IA offers a lower price per GB than S3 Standard. Since the data will be accessed infrequently after 30 days, using S3 Standard-IA will lower storage costs while still allowing for immediate retrieval when necessary.

Compliance with Regulations: Since the records need to be immediately accessible for the first 30 days, the use of S3 Standard for that period ensures compliance with regulatory requirements. After 30 days, transitioning to S3 Standard-IA continues to meet access requirements for infrequent access while reducing storage costs.

Alternatives Considered:

Option B (S3 Intelligent-Tiering): While S3 Intelligent-Tiering automatically moves data between access tiers based on access patterns, it incurs a small monthly monitoring and automation charge per object. It could be a viable option, but transitioning data to S3 Standard-IA directly would be more cost-effective since the pattern of access is well-known (frequent for 30 days, infrequent thereafter).

Option C (S3 Glacier Deep Archive): Glacier Deep Archive is the lowest-cost storage class, but it is not suitable in this case because the data needs to be accessed immediately within 30 days and on an infrequent basis thereafter. Glacier Deep Archive requires hours for data retrieval, which is not acceptable for infrequent access needs.

Option D (S3 Standard-IA for all records): Using S3 Standard-IA for all records would result in higher costs for the first 30 days, as the data is frequently accessed. S3 Standard-IA incurs retrieval charges, making it less suitable for frequently accessed data.

Amazon S3 Lifecycle Policies

S3 Storage Classes

Cost Management and Data Optimization Using Lifecycle Policies

AWS Data Engineering Documentation

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

The data engineer needs to orchestrate ETL jobs that include Spark jobs on Amazon EMR, API calls to Salesforce, and loading data into Redshift. They also need automatic failure handling and retries. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is the best solution for this requirement.

Option A: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)Apache Airflow is designed for complex job orchestration, allowing users to define workflows (DAGs) in Python. MWAA manages Airflow and its integrations with other AWS services, including Amazon EMR, Redshift, and external APIs like Salesforce. It provides automatic retry handling, failure detection, and detailed monitoring, which fits the use case perfectly.

Option B (AWS Step Functions) can orchestrate tasks but doesn't natively support complex workflow definitions with Python like Airflow does.

Option C (AWS Glue) is more focused on ETL and doesn't handle the orchestration of external systems like Salesforce as well as Airflow.

Option D (Amazon EventBridge) is more suited for event-driven architectures rather than complex workflow orchestration.