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| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Backup, restore, and disaster recovery - Automate operational tasks - Ensure reliability and scalability - Monitor and troubleshoot data pipelines
|
| Data Store Management | 26% | - Optimize storage performance and cost - Design and implement data storage solutions
|
| Data Ingestion and Transformation | 34% | - Implement data quality and validation - Ingest data from various sources
|
| Data Security and Governance | 18% | - Encrypt data at rest and in transit - Implement access control and authentication
- Enforce compliance and data governance
|
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NEW QUESTION # 285
A company has a data lake in Amazon 53. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.
The company needs to ensure that data quality issues are checked every time the pipelines run. A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.
Which solution will meet these requirements with the LEAST implementation effort?
Answer: C
Explanation:
* Problem Analysis:
* The company uses AWS Glue for ETL pipelines and must enforcedata quality checksduring pipeline execution.
* The goal is to implement quality checks withminimal implementation effort.
* Key Considerations:
* AWS Glue provides anEvaluate Data Quality transformthat 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-inEvaluate Data Quality transformis 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 theEvaluate Data Quality transformwithDQDLto implement data quality rules in AWS Glue pipelines.
:
AWS Glue Data Quality
DQDL Syntax and Examples
AWS Glue Studio Documentation
NEW QUESTION # 286
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.
Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.
Which factors could have caused the reordering system to receive duplicated data? (Select TWO.)
Answer: B,E
Explanation:
* Problem Analysis:
* The company usesKinesis Data Streamsfor both inventory management and reordering.
* TheKinesis Producer Library (KPL)publishes data, and theKinesis Client Library (KCL) consumes data.
* Duplicate records were observed in the inventory reordering system.
* Key Considerations:
* Kinesis streams are designed for durability but may produce duplicates under certain conditions.
* Factors such asnetwork timeouts,shard splits, or changes inrecord processorscan cause duplication.
* Solution Analysis:
* Option A: Network-Related Timeouts
* If the producer (KPL) experiences network timeouts, it retries data submission, potentially causing duplicates.
* Option B: High IteratorAgeMilliseconds
* High iterator age suggests delays in processing but does not directly cause duplication.
* Option C: Changes in Shards or Processors
* Changes in the number of shards or record processors can lead to re-processing of records, causing duplication.
* Option D: AggregationEnabled Set to True
* AggregationEnabled controls the aggregation of multiple records into one, but it does not cause duplication.
* Option E: High max_records Value
* A high max_records value increases batch size but does not lead to duplication.
* Final Recommendation:
* Network-related timeoutsandchanges in shards or processorsare the most likely causes of duplicate data in this scenario.
:
Amazon Kinesis Data Streams Best Practices
Kinesis Producer Library (KPL) Overview
Kinesis Client Library (KCL) Overview
NEW QUESTION # 287
A company wants to build a dimension table in an Amazon S3 bucket. The bucket contains historical data that includes 10 million records. The historical data is 1 TB in size.
A data engineer needs a solution to update changes for up to 10,000 records in the base table every day.
Which solution will meet this requirement with the LOWEST runtime?
Answer: D
Explanation:
Option D provides the lowest runtime because it uses a table format designed for efficient incremental upserts on Amazon S3, rather than repeatedly scanning and rewriting large portions of a 1 TB dataset. Although Spark on its own (Options A and C) can perform joins/merges, updating files stored in S3 typically requires expensive rewrites, especially as data grows. By contrast, Apache Hudi is purpose-built for maintaining large datasets on object storage with incremental updates, which directly fits "update up to 10,000 records every day" without reprocessing the full historical footprint.
For the compute layer, the document highlights that Amazon EMR provides a fully managed environment for running Apache Spark and other big data frameworks to process and analyze large datasets, making it appropriate for high-scale processing where performance matters. This is a better fit than using Pandas on 1 TB (Option B), which is not designed for distributed processing at that scale.
Therefore, combining EMR + Spark with an incremental storage framework (Hudi) is the most runtime- efficient approach for daily record-level updates on S3.
NEW QUESTION # 288
A retail company needs to implement a solution to capture data updates from multiple Amazon Aurora MySQL databases. The company needs to make the updates available for analytics in near real time. The solution must be serverless and require minimal maintenance.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
Option D is correct because Aurora zero-ETL integration with Amazon Redshift is the AWS-managed feature built specifically to make transactional Aurora data available in Amazon Redshift Serverless in near real time.
AWS documents that Aurora zero-ETL is a fully managed solution that makes transactional data available in the analytics destination after it is written to the Aurora cluster, eliminating the need to build and maintain complex ETL pipelines. AWS also states that Redshift zero-ETL supports a target data warehouse that can be a Redshift Serverless workgroup, which directly matches the question.
Option A and C add more operational components, such as DMS tasks, replication design, schema handling, and additional streaming infrastructure. Option B is also more operationally heavy because MSK Connect with Debezium requires Kafka-based CDC infrastructure and connector management. Since the requirement emphasizes serverless, near real-time analytics, and least operational overhead, the native zero-ETL integration is the best fit. This also aligns with the study guide's focus on choosing AWS services that minimize management effort while supporting ingestion and analytics workflows.
NEW QUESTION # 289
A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.
The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.
Which solution will meet these requirements with the LEAST operational overhead?
Answer:
Explanation:
A."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Although this reference directly supports using AWS Glue's monitoring features via EventBridge, it implies that solutions like A-which directly use EventBridge failure events for automation-are more optimal and less complex than constructing custom logs and metrics.
NEW QUESTION # 290
......
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