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최신 AWS Certified Data Engineer Data-Engineer-Associate 무료샘플문제 (Q225-Q230):

질문 # 225
A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?

정답：A

설명：
The best solution to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs is to use the query result reuse feature of Amazon Athena for the SQL queries. This feature allows you to run the same query multiple times without incurring additional charges, as long as the underlying data has not changed and the query results are still in the query result location in Amazon S31.
This feature is useful for scenarios where you have a petabyte-scale dataset that is updated infrequently, such as once a day, and you have a BI application that runs the same queries repeatedly, such as every hour. By using the query result reuse feature, you can reduce the amount of data scanned by your queries and save on the cost of running Athena. You can enable or disable this feature at the workgroup level or at the individual query level1.
Option A is not the best solution, as configuring an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day would not cost optimize the company's use of Amazon Athena, but rather increase the cost and complexity. Amazon S3 Lifecycle policies are rules that you can define to automatically transition objects between different storage classes based on specified criteria, such as the age of the object2. S3 Glacier Deep Archive is the lowest-cost storage class in Amazon S3, designed for long-term data archiving that is accessed once or twice in a year3. While moving data to S3 Glacier Deep Archive can reduce the storage cost, it would also increase the retrieval cost and latency, as it takes up to 12 hours to restore the data from S3 Glacier Deep Archive3. Moreover, Athena does not support querying data that is in S3 Glacier or S3 Glacier Deep Archive storage classes4. Therefore, using this option would not meet the requirements of running on-demand SQL queries on the dataset.
Option C is not the best solution, as adding an Amazon ElastiCache cluster between the BI application and Athena would not cost optimize the company's use of Amazon Athena, but rather increase the cost and complexity. Amazon ElastiCache is a service that offers fully managed in-memory data stores, such as Redis and Memcached, that can improve the performance and scalability of web applications by caching frequently accessed data. While using ElastiCache can reduce the latency and load on the BI application, it would not reduce the amount of data scanned by Athena, which is the main factor that determines the cost of running Athena. Moreover, using ElastiCache would introduce additional infrastructure costs and operational overhead, as you would have to provision, manage, and scale the ElastiCache cluster, and integrate it with the BI application and Athena.
Option D is not the best solution, as changing the format of the files that are in the dataset to Apache Parquet would not cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs, but rather increase the complexity. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However, changing the format of the files that are in the dataset to Apache Parquet would require additional processing and transformation steps, such as using AWS Glue or Amazon EMR to convert the files from their original format to Parquet, and storing the converted files in a separate location in Amazon S3. This would increase the complexity and the operational overhead of the data pipeline, and also incur additional costs for using AWS Glue or Amazon EMR. References:
* Query result reuse
* Amazon S3 Lifecycle
* S3 Glacier Deep Archive
* Storage classes supported by Athena
* [What is Amazon ElastiCache?]
* [Amazon Athena pricing]
* [Columnar Storage Formats]
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


질문 # 226
A data engineer is building a serverless, multi-step extract, transform, and load (ETL) pipeline. The pipeline extracts data from an Amazon S3 data lake and transforms the data by using AWS Glue ETL jobs. The pipeline then loads the results into an Amazon Redshift database. The data engineer needs to orchestrate the serverless ETL workflow.
Which solutions will meet these requirements? (Select TWO.)

정답：A,B

설명：
Options A and B are correct because both are managed, serverless orchestration approaches for AWS Glue ETL pipelines.
AWS Step Functions is a serverless orchestration service that can coordinate AWS services, including Glue jobs, as a sequence of steps. AWS documentation states that Step Functions supports built-in error handling with Retry and Catch, which directly fits the requirement to coordinate ETL jobs and handle failures automatically. AWS Prescriptive Guidance also shows ETL pipelines that move data from Amazon S3 through AWS Glue and onward in a Step Functions workflow.
AWS Glue workflows are also correct because AWS documentation states that Glue workflows can create and visualize complex ETL activities involving multiple jobs, crawlers, and triggers. They provide a graph of dependencies and can manage chained execution of interdependent tasks. That matches the requirement for serverless ETL orchestration.
Option C is not serverless because it requires an always-on EC2 instance. Option D can start jobs from events, but it is not the best service for managing complex chained dependencies and workflow state. Option E is primarily a CI/CD orchestration service, not the best fit for ETL job orchestration.


질문 # 227
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.
The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?

정답：C

설명：
Lambda layers are a way to share code and dependencies across multiple Lambda functions. By packaging the custom Python scripts into Lambda layers, the data engineer can update the scripts in one place and have them automatically applied to all the Lambda functions that use the layer. This reduces the manual effort and ensures consistency across the Lambda functions. The other options are either not feasible or not efficient. Storing a pointer to the custom Python scripts in the execution context object or in environment variables would require the Lambda functions to download the scripts from Amazon S3 every time they are invoked, which would increase latency and cost. Assigning the same alias to each Lambda function would not help with updating the Python scripts, as the alias only points to a specific version of the Lambda function code. Reference:
AWS Lambda layers
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.4: AWS Lambda


질문 # 228
A company runs an AWS Glue workflow every day to process time series data from an Amazon S3 bucket.
The workflow loads the data into an Amazon Redshift Serverless table. The company observes that some of the jobs in the workflow occasionally fail.
A data engineer must receive a notification when the Redshift table does not contain the most recent data.
Which solution will meet this requirement in the MOST operationally efficient way?

정답：A

설명：
Option B is the most operationally efficient because it checks the business requirement directly: whether the target table contains the most recent data, not merely whether a job failed. Monitoring only failures (Options C and D) can produce false positives (a job failure might not impact freshness) and false negatives (a job can succeed but still load stale or incomplete data). The study material emphasizes implementing data quality validation as part of the ETL process so data can be verified before or as it is stored, rather than relying only on pipeline execution status.
Using a data quality rule focused on freshness (for example, validating that a "max event timestamp" or "latest partition date" meets today's expected value) lets the pipeline detect stale loads even when the workflow runs. Then, an EventBridge rule can route failures of that data quality check to SNS for immediate notification, keeping operations serverless and centralized. Macie (Option A) is designed for sensitive-data discovery/classification, not operational "freshness" checks on Redshift tables, so it adds unnecessary services and effort compared to a Glue-native data quality validation approach.


질문 # 229
A company has a gaming application that stores data in Amazon DynamoDB tables. A data engineer needs to ingest the game data into an Amazon OpenSearch Service cluster. Data updates must occur in near real time.
Which solution will meet these requirements?

정답：C

설명：
* Problem Analysis:
* The company usesDynamoDBfor gaming data storage and needs to ingest data intoAmazon OpenSearch Serviceinnear real time.
* Data updates must propagate quickly to OpenSearch for analytics or search purposes.
* Key Considerations:
* DynamoDB Streamsprovide near-real-time capture of table changes (inserts, updates, and deletes).
* Integration withAWS Lambdaallows seamless processing of these changes.
* OpenSearch offers APIs for indexing and updating documents, which Lambda can invoke.
* Solution Analysis:
* Option A: Step Functions with Periodic Export
* Not suitable for near-real-time updates; introduces significant latency.
* Operationally complex to manage periodic exports and S3 data ingestion.
* Option B: AWS Glue Job
* AWS Glue is designed for ETL workloads but lacks real-time processing capabilities.
* Option C: DynamoDB Streams + Lambda
* DynamoDB Streams capture changes in near real time.
* Lambda can process these streams and use the OpenSearch API to update the index.
* This approach provides low latency and seamless integration with minimal operational overhead.
* Option D: Custom OpenSearch Plugin
* Writing a custom plugin adds complexity and is unnecessary with existing AWS integrations.
* Implementation Steps:
* EnableDynamoDB Streamsfor the relevant DynamoDB tables.
* Create aLambda functionto process stream records:
* Parse insert, update, and delete events.
* Use OpenSearch APIs to index or update documents based on the event type.
* Set up a trigger to invoke the Lambda function whenever there are changes in the DynamoDB Stream.
* Monitor and log errors for debugging and operational health.
:
Amazon DynamoDB Streams Documentation
AWS Lambda and DynamoDB Integration
Amazon OpenSearch Service APIs


질문 # 230
......

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