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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Store Management | 26% | - Manage data lifecycle
|
| Topic 2: Data Ingestion and Transformation | 34% | - Transform and process data
|
| Topic 3: Data Operations and Support | 22% | - Automate data pipelines
|
| Topic 4: Data Security and Governance | 18% | - Manage data privacy and compliance
|
>> Data-Engineer-Associate Übungsmaterialien <<
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209. Frage
A company wants to use Apache Spark jobs that run on an Amazon EMR cluster to process streaming data.
The Spark jobs will transform and store the data in an Amazon S3 bucket. The company will use Amazon Athena to perform analysis.
The company needs to optimize the data format for analytical queries.
Which solutions will meet these requirements with the SHORTEST query times? (Select TWO.)
Antwort: A,E
Begründung:
For high-performance analytics in Athena and EMR Spark, the columnar storage formats Parquet and ORC deliver the shortest query times. These formats minimize I/O, enable predicate pushdown, and support efficient compression and parallel reads.
When integrated with the AWS Glue Data Catalog, schema evolution is automatically tracked and managed across queries.
This combination (Parquet/ORC + Glue Catalog) is explicitly recommended by AWS for analytical workloads on S3 data lakes.
"For analytical workloads using Amazon EMR and Athena, store data in columnar formats such as Parquet or ORC. These formats provide optimized compression and query performance."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
"Use the AWS Glue Data Catalog to manage table schemas and schema evolution for columnar data formats such as Parquet and ORC."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Therefore, using ORC or Parquet along with the Glue Data Catalog achieves the shortest Athena query times and seamless schema management.
210. Frage
A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.
Which solution will meet these requirements with the LEAST operational overhead?
Antwort: C
Begründung:
This solution meets the requirements of managing the ingestion of real-time streaming data into AWS and performing real-time analytics on the incoming streaming data with the least operational overhead. Amazon Managed Service for Apache Flink is a fully managed service that allows you to run Apache Flink applications without having to manage any infrastructure or clusters. Apache Flink is a framework for stateful stream processing that supports various types of aggregations, such as tumbling, sliding, and session windows, over streaming data. By using Amazon Managed Service for Apache Flink, you can easily connect to Amazon Kinesis Data Streams as the source and sink of your streaming data, and perform time-based analytics over a window of up to 30 minutes. This solution is also highly fault tolerant, as Amazon Managed Service for Apache Flink automatically scales, monitors, and restarts your Flink applications in case of failures. Reference:
Amazon Managed Service for Apache Flink
Apache Flink
Window Aggregations in Flink
211. Frage
A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.
A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.
The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?
Antwort: C
Begründung:
To achieve the most cost-effective storage solution, the data engineer needs to use an S3 Lifecycle policy that transitions objects to lower-cost storage classes based on their access patterns, and deletes them when they are no longer needed. The storage classes should also provide high availability, which means they should be resilient to the loss of data in a single Availability Zone1. Therefore, the solution must include the following steps:
Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is designed for data that is accessed less frequently, but requires rapid access when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost and a retrieval fee2.
Therefore, it is suitable for data files that are accessed once or twice each month. S3 Standard-IA also provides high availability, as it stores data redundantly across multiple Availability Zones1.
Transfer objects to S3 Glacier Deep Archive after 2 years. S3 Glacier Deep Archive is the lowest-cost storage class that offers secure and durable storage for data that is rarely accessed and can tolerate a 12-hour retrieval time. It is ideal for long-term archiving and digital preservation3. Therefore, it is suitable for data files that are accessed only once or twice each year. S3 Glacier Deep Archive also provides high availability, as it stores data across at least three geographically dispersed Availability Zones1.
Delete objects when they are no longer needed. The data engineer can specify an expiration action in the S3 Lifecycle policy to delete objects after a certain period of time. This will reduce the storage cost and comply with any data retention policies.
Option C is the only solution that includes all these steps. Therefore, option C is the correct answer.
Option A is incorrect because it transitions objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after
6 months. S3 One Zone-IA is similar to S3 Standard-IA, but it stores data in a single Availability Zone. This means it has a lower availability and durability than S3 Standard-IA, and it is not resilient to the loss of data in a single Availability Zone1. Therefore, it does not provide high availability as required.
Option B is incorrect because it transfers objects to S3 Glacier Flexible Retrieval after 2 years. S3 Glacier Flexible Retrieval is a storage class that offers secure and durable storage for data that is accessed infrequently and can tolerate a retrieval time of minutes to hours. It is more expensive than S3 Glacier Deep Archive, and it is not suitable for data that is accessed only once or twice each year3. Therefore, it is not the most cost-effective option.
Option D is incorrect because it combines the errors of option A and B. It transitions objects to S3 One Zone- IA after 6 months, which does not provide high availability, and it transfers objects to S3 Glacier Flexible Retrieval after 2 years, which is not the most cost-effective option.
1: Amazon S3 storage classes - Amazon Simple Storage Service
2: Amazon S3 Standard-Infrequent Access (S3 Standard-IA) - Amazon Simple Storage Service
3: Amazon S3 Glacier and S3 Glacier Deep Archive - Amazon Simple Storage Service
[4]: Expiring objects - Amazon Simple Storage Service
[5]: Managing your storage lifecycle - Amazon Simple Storage Service
[6]: Examples of S3 Lifecycle configuration - Amazon Simple Storage Service
[7]: Amazon S3 Lifecycle further optimizes storage cost savings with new features - What's New with AWS
212. Frage
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?
Antwort: C
Begründung:
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.
213. Frage
The company stores a large volume of customer records in Amazon S3. To comply with regulations, the company must be able to access new customer records immediately for the first 30 days after the records are created. The company accesses records that are older than 30 days infrequently.
The company needs to cost-optimize its Amazon S3 storage.
Which solution will meet these requirements MOST cost-effectively?
Antwort: D
Begründung:
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
214. Frage
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