그리고 Pass4Test Data-Engineer-Associate 시험 문제집의 전체 버전을 클라우드 저장소에서 다운로드할 수 있습니다: https://drive.google.com/open?id=1IMmIqc-3rvk8H5Ye00gyvd6g0M-o1C2o
Pass4Test의Amazon인증 Data-Engineer-Associate덤프를 공부하시면 한방에 시험을 패스하는건 문제가 아닙니다. Pass4Test의Amazon인증 Data-Engineer-Associate덤프는 시험적중율 최고의 인지도를 넓히 알리고 있습니다.저희가 제공한 시험예상문제로 시험에 도전해보지 않으실래요? Amazon인증 Data-Engineer-Associate덤프를 선택하시면 성공의 지름길이 눈앞에 다가옵니다.
| Section | Weight | Objectives |
|---|---|---|
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
>> Data-Engineer-Associate높은 통과율 인기덤프 <<
한번에Amazon인증Data-Engineer-Associate시험을 패스하고 싶으시다면 완전 페펙트한 준비가 필요합니다. 완벽한 관연 지식터득은 물론입니다. 우리Pass4Test의 자료들은 여러분의 이런 시험준비에 많은 도움이 될 것입니다.
질문 # 168
A data engineer wants to optimize the runtime performance of an AWS Glue extract, transform, and load (ETL) job. The job processes large JSON files from Amazon S3. The job currently reads all fields from the source files but transforms only a subset of the fields.
Which solution will meet these requirements?
정답：D
설명：
The correct answer is B because the performance issue is caused by reading unnecessary data. The best optimization is to reduce the amount of data and schema fields that the AWS Glue job reads before transformation. Pushdown-style filtering and selecting only required fields reduce scan and serialization overhead, especially when the job transforms only a subset of the JSON structure. Job bookmarks help track previously processed data, but they do not reduce the amount of data read within each file. Splitting the job into several smaller jobs increases orchestration and does not solve unnecessary field reads. An AWS Glue crawler catalogs metadata; it does not convert JSON files into Parquet. Therefore, restricting the source schema and pushing filtering closer to the source is the best available answer.
질문 # 169
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.
The company receives 2 GB erf sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.
Which solution will meet these requirements with the LEAST operational overhead?
정답：C
설명：
Problem Analysis:
The company processes 2 GB of daily sales records and 100 GB of Salesforce sales opportunities.
The goal is to analyze and correlate the two datasets with low operational overhead.
The process must run once nightly.
Key Considerations:
Amazon AppFlow simplifies data integration with Salesforce.
AWS Glue can extract data from MySQL and perform ETL operations.
Step Functions can orchestrate workflows with minimal manual intervention.
Apache Airflow and Flink add complexity, which conflicts with the requirement for low operational overhead.
Solution Analysis:
Option A: MWAA + Lambda + Step Functions
Requires custom Lambda code for dataset correlation, increasing development and operational complexity.
Option B: AppFlow + Glue + MWAA
MWAA adds orchestration overhead compared to the simpler Step Functions.
Option C: AppFlow + Glue + Step Functions
AppFlow fetches Salesforce data, Glue extracts MySQL data, and Step Functions orchestrate the entire process.
Minimal setup and operational overhead, making it the best choice.
Option D: AppFlow + Kinesis + Flink + Step Functions
Using Kinesis and Flink for batch processing introduces unnecessary complexity.
Final Recommendation:
Use Amazon AppFlow to fetch Salesforce data, AWS Glue to process MySQL data, and Step Functions for orchestration.
Reference:
Amazon AppFlow Overview
AWS Glue ETL Documentation
AWS Step Functions
질문 # 170
A data engineer needs to deploy a complex pipeline. The stages of the pipeline must run scripts, but only fully managed and serverless services can be used.
정답：A
설명：
AWS Glue is a fully managed and serverless ETL platform that supports scripts in PySpark or Python shell jobs.
Workflows in Glue orchestrate multiple job stages without infrastructure management.
"Use AWS Glue Workflows to build and orchestrate complex multi-stage ETL pipelines using serverless AWS Glue jobs."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This meets the "serverless only" and "runs scripts" requirements precisely.
질문 # 171
A company stores server logs in an Amazon 53 bucket. The company needs to keep the logs for 1 year. The logs are not required after 1 year.
A data engineer needs a solution to automatically delete logs that are older than 1 year.
Which solution will meet these requirements with the LEAST operational overhead?
정답：D
설명：
Problem Analysis:
The company uses AWS Glue for ETL pipelines and requires automatic data quality checks during pipeline execution.
The solution must integrate with existing AWS Glue pipelines and evaluate data quality rules based on predefined thresholds.
Key Considerations:
Ensure minimal implementation effort by leveraging built-in AWS Glue features.
Use a standardized approach for defining and evaluating data quality rules.
Avoid custom libraries or external frameworks unless absolutely necessary.
Solution Analysis:
Option A: SQL Transform
Adding SQL transforms to define and evaluate data quality rules is possible but requires writing complex queries for each rule.
Increases operational overhead and deviates from Glue's declarative approach.
Option B: Evaluate Data Quality Transform with DQDL
AWS Glue provides a built-in Evaluate Data Quality transform.
Allows defining rules in Data Quality Definition Language (DQDL), a concise and declarative way to define quality checks.
Fully integrated with Glue Studio, making it the least effort solution.
Option C: Custom Transform with PyDeequ
PyDeequ is a powerful library for data quality checks but requires custom code and integration.
Increases implementation effort compared to Glue's native capabilities.
Option D: Custom Transform with Great Expectations
Great Expectations is another powerful library for data quality but adds complexity and external dependencies.
Final Recommendation:
Use Evaluate Data Quality transform in AWS Glue.
Define rules in DQDL for checking thresholds, null values, or other quality criteria.
This approach minimizes development effort and ensures seamless integration with AWS Glue.
Reference:
AWS Glue Data Quality Overview
DQDL Syntax and Examples
Glue Studio Transformations
질문 # 172
A data engineer is processing a large amount of log data from web servers. The data is stored in an Amazon S3 bucket. The data engineer uses AWS services to process the data every day. The data engineer needs to extract specific fields from the raw log data and load the data into a data warehouse for analysis.
정답：B
질문 # 173
......
지금 같은 세대에 많은 분들이 IT업계에 관심을 가지고 있습니다. 이렇게 인재가 많은 사회에서 IT관련인사들은 아직도 적은 편입니다. 면접 시에도 IT인증 자격증유무를 많이들 봅니다. 때문에 IT자격증이 많은 인기를 누리고 있습니다.이런 살아가기 힘든 사회에서 이런 자격증들 또한 취득하기가 넘 어렵습니다.Amazon Data-Engineer-Associate인증시험 또한 아주 어려운 시험입니다. 많은 분들이 응시하지만 통과하는 분들은 아주 적습니다.
Data-Engineer-Associate퍼펙트 덤프공부자료: https://www.pass4test.net/Data-Engineer-Associate.html
참고: Pass4Test에서 Google Drive로 공유하는 무료, 최신 Data-Engineer-Associate 시험 문제집이 있습니다: https://drive.google.com/open?id=1IMmIqc-3rvk8H5Ye00gyvd6g0M-o1C2o