참고: Itcertkr에서 Google Drive로 공유하는 무료 2026 Amazon Data-Engineer-Associate 시험 문제집이 있습니다: https://drive.google.com/open?id=1dgp1HAgayGFPoYqaW9OCdl8XzxXWcNiI
IT업계의 치열한 경쟁속에 살아 남으려면 자신의 능력을 증명하여야 합니다. 국제승인을 받는 IT인증자격증을 많이 취득하시면 취직이든 승진이든 이직이든 모든 면에서 이득을 볼수 있습니다. 최근 Amazon인증 Data-Engineer-Associate시험에 도전하는 분이 많은데 Itcertkr에서 Amazon인증 Data-Engineer-Associate시험에 대비한 가장 최신버전 덤프공부가이드를 제공해드립니다.
| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Manage data lifecycle and storage tiers - Optimize storage performance and cost - Design and implement data storage solutions
|
| Data Security and Governance | 18% | - Implement access control and authentication
- Enforce compliance and data governance
|
| Data Operations and Support | 22% | - Monitor and troubleshoot data pipelines
- Backup, restore, and disaster recovery - Ensure reliability and scalability |
| Data Ingestion and Transformation | 34% | - Transform and enrich data
- Ingest data from various sources
|
>> Amazon Data-Engineer-Associate높은 통과율 시험덤프공부 <<
IT업계의 치열한 경쟁속에 살아 남으려면 자신의 능력을 증명하여야 합니다. 국제승인을 받는 IT인증자격증을 많이 취득하시면 취직이든 승진이든 이직이든 모든 면에서 이득을 볼수 있습니다. 최근 Amazon인증 Data-Engineer-Associate시험에 도전하는 분이 많은데 Itcertkr에서 Amazon인증 Data-Engineer-Associate시험에 대비한 가장 최신버전 덤프공부가이드를 제공해드립니다.
질문 # 262
Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository's master branch as the source.
The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week's scheduled application release.
Which command should the developer for Branch B run before the developer raises a pull request to the master branch?
정답：B
설명：
To ensure that Branch B is up to date with the latest changes in the master branch before submitting a pull request, the correct approach is to perform a git rebase. This command rewrites the commit history so that Branch B will be based on the latest changes in the master branch.
git rebase master:
This command moves the commits of Branch B to be based on top of the latest state of the master branch. It allows the developer to resolve any conflicts and create a clean history.
Reference: Git Rebase Documentation
Alternatives Considered:
A (git diff): This will only show differences between Branch B and master but won't resolve conflicts or bring Branch B up to date.
B (git pull master): Pulling the master branch directly does not offer the same clean history management as rebase.
D (git fetch -b): This is an incorrect command.
References:
Git Rebase Best Practices
질문 # 263
A company builds a new data pipeline to process data for business intelligence reports. Users have noticed that data is missing from the reports.
A data engineer needs to add a data quality check for columns that contain null values and for referential integrity at a stage before the data is added to storage.
Which solution will meet these requirements with the LEAST operational overhead?
정답：D
설명：
AWS Glue Data Quality transforms allow you to define built-in rules like IsComplete for null validation and ReferentialIntegrity for relationship validation-all with minimal code and operational overhead.
"Use AWS Glue Data Quality rules such as IsComplete and ReferentialIntegrity within ETL jobs to automatically validate incoming data."
질문 # 264
A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?
정답：A
설명：
The Amazon Redshift Data API is a built-in feature that allows you to run SQL queries on Amazon Redshift data with web services-based applications, such as AWS Lambda, Amazon SageMaker notebooks, and AWS Cloud9. The Data API does not require a persistent connection to your database, and it provides a secure HTTP endpoint and integration with AWS SDKs. You can use the endpoint to run SQL statements without managing connections. The Data API also supports both Amazon Redshift provisioned clusters and Redshift Serverless workgroups. The Data API is the best solution for running real-time queries on the financial data from within the trading application, as it has the least operational overhead compared to the other options.
Option A is not the best solution, as establishing WebSocket connections to Amazon Redshift would require more configuration and maintenance than using the Data API. WebSocket connections are also not supported by Amazon Redshift clusters or serverless workgroups.
Option C is not the best solution, as setting up JDBC connections to Amazon Redshift would also require more configuration and maintenance than using the Data API. JDBC connections are also not supported by Redshift Serverless workgroups.
Option D is not the best solution, as storing frequently accessed data in Amazon S3 and using Amazon S3 Select to run the queries would introduce additional latency and complexity than using the Data API. Amazon S3 Select is also not optimized for real-time queries, as it scans the entire object before returning the results. Reference:
Using the Amazon Redshift Data API
Calling the Data API
Amazon Redshift Data API Reference
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
질문 # 265
A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. The ETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, and load data into Amazon Redshift.
The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python to orchestrate the jobs.
Which service will meet these requirements?
정답：A
설명：
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.
Reference:
Amazon Managed Workflows for Apache Airflow
Apache Airflow on AWS
질문 # 266
A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account. A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow. Which log type should the data engineer use to diagnose the cause of the failure?
정답：C
설명：
In Amazon Managed Workflows for Apache Airflow (MWAA), the type of log that is most useful for diagnosing workflow (DAG) failures is the Task logs. These logs provide detailed information on the execution of each task within the DAG, including error messages, exceptions, and other critical details necessary for diagnosing failures.
Option D: YourEnvironmentName-Task
Task logs capture the output from the execution of each task within a workflow (DAG), which is crucial for understanding what went wrong when a DAG fails. These logs contain detailed execution information, including errors and stack traces, making them the best source for debugging.
Other options (WebServer, Scheduler, and DAGProcessing logs) provide general environment-level logs or logs related to scheduling and DAG parsing, but they do not provide the granular task-level execution details needed for diagnosing workflow failures.
Reference:
Amazon MWAA Logging and Monitoring
Apache Airflow Task Logs
질문 # 267
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