AWS Certified Data Engineer - Associate (DEA-C01)

The AWS Certified Data Engineer – Associate (DEA-C01) validates your ability to design, build, and maintain robust data pipelines that ingest, transform, and store data securely on AWS. Launched in 2024, it replaced the previous "Data Analytics Specialty" as the primary certification for data-centric roles. 

 

---------- Question 1

A large organization has built a data lake on Amazon S3, accumulating data from various departments, including sales, marketing, and operations. The data is in different formats (CSV, JSON, Parquet) and has inconsistent naming conventions and schemas. Data analysts and data scientists struggle to discover relevant datasets, understand their structure, and identify reliable data sources, leading to inefficiencies and potential data misinterpretations. Which AWS service and associated practices should a data engineer implement to create a centralized repository for metadata, enable schema discovery, and facilitate self-service data discovery for the analytics team?

1. Manually maintain an Excel spreadsheet with dataset descriptions and S3 paths, sharing it with the analytics team.
2. Use Amazon Redshift Spectrum to query data directly from S3, relying on users to manually define external tables and schemas.
3. Implement AWS Glue Data Catalog as a central metadata repository. Use AWS Glue Crawlers to automatically scan S3 buckets, infer schemas from various data formats, and populate the catalog. Provide access to the Glue Data Catalog for data analysts and scientists via services like Amazon Athena and Amazon EMR.
4. Develop custom scripts to extract schema information from S3 objects and store it in an Amazon DynamoDB table, building a custom UI for data discovery.

---------- Question 2

A media company needs to ingest real-time social media mentions for trending topic analysis. The data volume can surge unexpectedly during major events, and historical data needs to be available for replayability in case of processing errors or new analytics requirements. They are considering Amazon Kinesis Data Streams for ingestion. Which combination of strategies and Kinesis features should a data engineer recommend to handle variable throughput, ensure data replayability, and support stateless processing of individual records?

1. Configure a Kinesis Data Stream with on-demand capacity mode and enable Kinesis Data Firehose for batch delivery to S3 for replayability, processing records individually with AWS Lambda for stateless operations.
2. Use a Kinesis Data Stream provisioned with maximum shards, implement a custom consumer application that stores offsets in DynamoDB for replayability, and process records using Apache Flink for stateful operations.
3. Employ Amazon MSK for ingestion due to its higher throughput capabilities, configure S3 Event Notifications for replayability, and use AWS Glue streaming ETL jobs for stateless transformations.
4. Set up a Kinesis Data Stream in provisioned mode with sufficient shards, enable Kinesis Data Analytics for real-time processing, and rely on Amazon S3 object versioning for replayability, processing records with Amazon EMR for batch transformations.

---------- Question 3

A global technology company processes vast amounts of customer data, including personally identifiable information PII, across its data pipelines. This data is stored in Amazon S3, processed by AWS Glue, and analyzed in Amazon Redshift. The company has a strict compliance requirement to encrypt all data at rest and in transit, and to mask or anonymize PII before it is made available to non-production environments or broader analytical teams. They need a robust solution that uses managed encryption keys and provides data masking capabilities. Which combination of AWS services and configurations would effectively meet these data encryption and PII masking requirements?

1. Use S3 server-side encryption with S3 managed keys SSE-S3 for data at rest, and implement custom Lambda functions for PII masking.
2. Enable Amazon Redshift encryption with AWS KMS managed keys, configure AWS Glue connections to use SSL/TLS for data in transit, and use AWS Glue DataBrew for PII masking.
3. Apply client-side encryption for all data uploaded to S3, rely on default AWS Glue encryption, and manually remove PII columns before loading into Redshift.
4. Use S3 bucket policies to enforce encryption, disable encryption in Redshift for performance, and rely on application-level filtering for PII.

---------- Question 4

A media streaming company stores petabytes of user interaction logs in an Amazon S3 bucket. These logs are frequently accessed for analysis for the first 30 days. After 30 days, they are rarely accessed but must be retained for compliance reasons for another 5 years at the lowest possible cost. After 5 years, the data can be permanently deleted. The company needs to automate the transition of these log files through different storage tiers and ensure eventual deletion, minimizing manual intervention and storage costs. What is the most efficient and automated AWS S3 capability to manage the lifecycle of this data, balancing access requirements with cost optimization and compliance?

1. Manually move older log files to S3 Glacier Deep Archive after 30 days and delete them after 5 years using custom scripts.
2. Configure an S3 Lifecycle policy to transition objects to S3 Standard-IA after 30 days, then to S3 Glacier Flexible Retrieval after 90 days, and finally expire them after 5 years and 30 days.
3. Configure an S3 Lifecycle policy to transition objects to S3 Standard-IA after 30 days, then to S3 Glacier Deep Archive after 90 days, and finally expire them after 5 years and 30 days.
4. Use S3 Intelligent-Tiering to automatically move data between access tiers, and then manually delete files after 5 years.

---------- Question 5

A data engineering team has deployed a mission-critical AWS Glue ETL pipeline that processes sensitive customer financial data daily. They need to ensure the pipeline executes successfully within a specified time window, quickly detect and address any failures or performance bottlenecks, and maintain a complete, immutable audit trail of all API calls made by the pipeline for regulatory compliance. The solution must provide immediate alerts for issues and enable detailed troubleshooting. Which combination of AWS services should the team implement for comprehensive monitoring, alerting, logging, and auditing?

1. Use Amazon CloudWatch Logs for all application logs, with AWS CloudTrail to track API calls, and Amazon SNS for notifications of critical events.
2. Manually inspect AWS Glue job logs in the console daily and set up custom shell scripts to check job statuses.
3. Integrate AWS X-Ray for tracing and debugging, storing all trace data in Amazon S3 for long-term audit.
4. Employ AWS Trusted Advisor for performance and security checks, and AWS Config to monitor resource changes.

---------- Question 6

A large enterprise has data spread across various sources, including Amazon S3 data lakes, Amazon Redshift data warehouses, and Amazon RDS relational databases. Data analysts and scientists frequently struggle to discover available datasets, understand their schemas, and identify data ownership or quality metrics, leading to duplicated efforts and incorrect analyses. The data engineering team needs to implement a centralized data cataloging solution that automatically discovers schema information, provides searchable metadata, and integrates with analytical services to improve data discoverability and usability across the organization. Which AWS service is best suited to build a centralized, searchable data catalog that spans multiple data sources and facilitates schema discovery and metadata management for analytical users?

1. Deploy a custom Apache Atlas instance on Amazon EC2 to create a metadata catalog and integrate it with various data sources using custom connectors.
2. Utilize AWS Glue Data Catalog to automatically crawl data sources like S3 and Redshift, populate metadata, and make schemas available for querying by services like Amazon Athena and Amazon EMR.
3. Implement Amazon DynamoDB as a metadata store, manually populating it with dataset descriptions and schemas, and building a custom web interface for users to search.
4. Leverage Amazon QuickSight to connect to all data sources and create dashboards, relying on QuickSight datasets to provide a form of data discoverability.

---------- Question 7

A logistics company has built several interconnected data pipelines that process shipment tracking events, update inventory, and generate daily reports. These pipelines involve multiple steps: ingesting data into S3, transforming it using AWS Glue, loading into Amazon Redshift, and finally generating reports with Amazon QuickSight. Some steps are event-driven, for example, new files arriving in S3, while others are scheduled daily or depend on the successful completion of previous steps. The company needs a robust, scalable, and fault-tolerant orchestration solution to manage these complex dependencies and workflows, providing visibility and error handling. Which AWS service is best suited for orchestrating these complex, multi-step, and potentially interdependent data pipelines, integrating both event-driven and scheduled components?

1. Manually trigger each AWS Glue job and Redshift COPY command using individual AWS CLI commands or custom scripts.
2. Use Amazon EventBridge to trigger all AWS Glue jobs and Redshift loads based on time-based schedules or S3 events directly.
3. Implement AWS Step Functions to define and manage the stateful workflows, integrating with AWS Glue, Amazon Redshift, and Amazon S3 events.
4. Utilize AWS Lambda functions to sequence and call other Lambda functions or services, handling retries and error logging within each function.

---------- Question 8

A global e-commerce platform experiences millions of customer clickstream events per second. This real-time data needs to be ingested, enriched with customer profile information from a separate transactional database, and then distributed to several downstream analytics applications for fraud detection and personalization engines. The solution must ensure low latency, high throughput, and the ability to reprocess historical data in case of errors. Which combination of AWS services and practices effectively meets these requirements for ingestion, enrichment, and distribution?

1. Use Amazon Kinesis Data Firehose to ingest data directly into S3, then trigger an AWS Glue job to enrich the data, and finally use Amazon SNS to fan out to multiple consumers.
2. Ingest clickstream data into Amazon Kinesis Data Streams, process and enrich it in real-time using AWS Lambda functions or Amazon Kinesis Data Analytics, and then use multiple Kinesis Data Streams consumers or Amazon SQS for distribution.
3. Deploy an Amazon MSK cluster for ingestion, use Amazon EMR to perform batch enrichment daily, and then expose the data through an Amazon RDS instance for downstream applications.
4. Use Amazon SQS for initial ingestion of events, trigger AWS Lambda to push data to Amazon DynamoDB for enrichment, and then rely on DynamoDB Streams to fan out to other services.

---------- Question 9

A media company operates a critical data pipeline consisting of three sequential steps: ingesting video metadata from an Amazon S3 bucket, processing this data with an AWS Glue ETL job, and finally loading the transformed data into Amazon Redshift. This pipeline must execute nightly, ensuring that each step successfully completes before the next one begins. The company also demands automated error detection, retry mechanisms, and notifications upon failure, all while favoring a serverless architecture to minimize operational overhead. Which AWS service is best suited to orchestrate this entire workflow?

1. Schedule individual AWS Glue jobs and Amazon Redshift LOAD commands using Amazon EventBridge time-based rules.
2. Implement an AWS Step Functions state machine to define and manage the sequential workflow logic.
3. Utilize Amazon Managed Workflows for Apache Airflow MWAA to orchestrate the pipeline with Python DAGs.
4. Configure Amazon SQS to queue messages between each step, triggering AWS Lambda functions for orchestration.

---------- Question 10

A marketing analytics team requires daily reports generated from customer interaction data stored in an Amazon S3 data lake. The process involves multiple steps: first, new raw data files are ingested into S3; second, an AWS Glue job processes and transforms this raw data into a cleaned, denormalized format; third, another AWS Glue job aggregates the transformed data; and finally, the aggregated data is loaded into an Amazon Redshift cluster for reporting. The data engineering team needs to fully automate this end-to-end pipeline, making it event-driven where possible, ensuring re-run capabilities, and providing an easy way to monitor the status of each step.

1. Use a cron job on an Amazon EC2 instance to execute a series of AWS CLI commands that trigger the AWS Glue jobs sequentially. This solution lacks robustness for dependency management, error handling, and serverless scalability, and requires EC2 instance management.
2. Configure Amazon EventBridge to trigger the first AWS Glue job on a schedule. Within the Glue job script, use boto3 SDK calls to trigger the subsequent Glue job, and so on. This creates a tight coupling between jobs, making the pipeline difficult to manage, visualize, and debug, and lacking inherent retry mechanisms for the overall workflow.
3. Create an AWS Step Functions state machine to orchestrate the entire workflow. The state machine should be triggered by Amazon EventBridge on a daily schedule. Define steps within the state machine to invoke AWS Glue jobs for transformation and aggregation, and AWS Lambda functions for S3 ingestion checks and Redshift loading. Configure retries and error handling within the state machine.
4. Deploy Apache Airflow on Amazon EKS and define a DAG (Directed Acyclic Graph) for the pipeline. While Airflow is powerful for workflow orchestration, deploying and managing it on EKS introduces significant operational overhead and complexity compared to fully managed AWS services for this specific scenario.

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