2026 Pass4Test 최신 Data-Engineer-Associate PDF 버전 시험 문제집과 Data-Engineer-Associate 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=11xoxQbR2jpLUCtAZyeVhzJntXIS01vQA
Amazon인증Data-Engineer-Associate시험을 패스함으로 취업에는 많은 도움이 됩니다. Pass4Test는Amazon인증Data-Engineer-Associate시험패스로 꿈을 이루어주는 사이트입니다. 우리는Amazon인증Data-Engineer-Associate시험의 문제와 답은 아주 좋은 학습자료로도 충분한 문제집입니다. 여러분이 안전하게 간단하게Amazon인증Data-Engineer-Associate시험을 응시할 수 있는 자료입니다.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Security and Governance | 18% | - Ensure data encryption
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| Topic 2: Data Operations and Support | 22% | - Manage and troubleshoot data processes
|
| Topic 3: Data Store Management | 26% | - Choose a data store
|
| Topic 4: Data Ingestion and Transformation | 34% | - Orchestrate data pipelines
|
>> Data-Engineer-Associate인증시험 공부자료 <<
Pass4Test의Amazon인증 Data-Engineer-Associate시험대비 덤프는 가격이 착한데 비하면 품질이 너무 좋은 시험전 공부자료입니다. 시험문제적중율이 높아 패스율이 100%에 이르고 있습니다.다른 IT자격증에 관심이 있는 분들은 온라인서비스에 문의하여 덤프유무와 적중율등을 확인할수 있습니다. Amazon인증 Data-Engineer-Associate덤프로 어려운 시험을 정복하여 IT업계 정상에 오릅시다.
질문 # 52
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：B
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 # 53
A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway.
Which solution will meet these requirements with the LEAST operational overhead?
정답：B
설명：
AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that perform custom logic and integrate with other AWS services, such as API Gateway. Lambda automatically scales your application by running code in response to each trigger. You pay only for the compute time you consume1.
Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. You can use ECS to deploy, manage, andscale Docker containers using either Amazon EC2 instances or AWS Fargate, a serverless compute engine for containers2.
Amazon EKS is a fully managed Kubernetes service that allows you to run Kubernetes clusters on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use EKS to deploy, manage, and scale containerized applications using Kubernetes on AWS3.
The solution that meets the requirements with the least operational overhead is to create an AWS Lambda Python function with provisioned concurrency. This solution has the following advantages:
* It does not require you to provision, manage, or scale any servers or clusters, as Lambda handles all the infrastructure for you. This reduces the operational complexity and cost of running your code.
* It allows you to write your Python script as a Lambda function and integrate it with API Gateway using a simple configuration. API Gateway can invoke your Lambda function synchronously or asynchronously, and return the results to the frontend website.
* It ensures that your Lambda function is ready to respond to API requests without any cold start delays, by using provisioned concurrency. Provisioned concurrency is a feature that keeps your function initialized and hyper-ready to respond in double-digit milliseconds. You can specify the number of concurrent executions that you want to provision for your function.
Option A is incorrect because it requires you to deploy a custom Python script on an Amazon ECS cluster.
This solution has the following disadvantages:
* It requires you to provision, manage, and scale your own ECS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.
* It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.
* It requires you to configure your ECS cluster to integrate with API Gateway, either using an Application Load Balancer or a Network Load Balancer. This adds another layer of complexity to your architecture.
Option C is incorrect because it requires you to deploy a custom Python script that can integrate with API Gateway on Amazon EKS. This solution has the following disadvantages:
* It requires you to provision, manage, and scale your own EKS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.
* It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.
* It requires you to configure your EKS cluster to integrate with API Gateway, either using an Application Load Balancer, a Network Load Balancer, or a service of type LoadBalancer. This adds another layer of complexity to your architecture.
Option D is incorrect because it requires you to create an AWS Lambda function and ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events. This solution has the following disadvantages:
* It does not guarantee that your Lambda function will always be warm, as Lambda may scale down your function if it does not receive any requests for a long period of time. This may cause cold start delays when your function is invoked by API Gateway.
* It incurs unnecessary costs, as you pay for the compute time of your Lambda function every time it is invoked by the EventBridge rule, even if it does not perform any useful work1.
:
1: AWS Lambda - Features
2: Amazon Elastic Container Service - Features
3: Amazon Elastic Kubernetes Service - Features
[4]: Building API Gateway REST API with Lambda integration - Amazon API Gateway
[5]: Improving latency with Provisioned Concurrency - AWS Lambda
[6]: Integrating Amazon ECS with Amazon API Gateway - Amazon Elastic Container Service
[7]: Integrating Amazon EKS with Amazon API Gateway - Amazon Elastic Kubernetes Service
[8]: Managing concurrency for a Lambda function - AWS Lambda
질문 # 54
A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.
The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.
A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.
Which solution will meet these requirements with the LEAST operational effort?
정답：B
설명：
Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers the least operational effort by avoiding complex data movement and transformation processes.
Amazon Athena Federated Queries:
Athena's federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena's support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.
The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.
Reference:
Alternatives Considered:
A (AWS Glue pipeline): This would work but requires more operational effort to manage and transform the data in AWS Glue.
C (Amazon EMR): Using EMR and writing PySpark code introduces more operational overhead and complexity compared to a SQL-based solution in Athena.
D (Amazon AppFlow): AppFlow is more suitable for transferring data between services but is not as efficient for transformations and joins as Athena federated queries.
Amazon Athena Documentation
Federated Queries in Amazon Athena
질문 # 55
A company has an Amazon Redshift data warehouse that users access by using a variety of IAM roles. More than 100 users access the data warehouse every day.
The company wants to control user access to the objects based on each user's job role, permissions, and how sensitive the data is.
Which solution will meet these requirements?
정답：D
설명：
Amazon Redshift supports Role-Based Access Control (RBAC) to manage access to database objects.
RBAC allows administrators to create roles for job functions and assign privileges at the schema, table, or column level based on data sensitivity and user roles.
"RBAC in Amazon Redshift helps manage permissions more efficiently at scale by assigning users to roles that reflect their job function. It simplifies user management and secures access based on job role and data sensitivity."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf RBAC is preferred over RLS or CLS alone because it offers a more comprehensive and scalable solution across multiple users and permissions.
질문 # 56
A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?
정답：C
설명：
AWS Glue is a fully managed serverless ETL service that can handle data deduplication with minimal operational overhead. AWS Glue provides a built-in ML transform called FindMatches, which can automatically identify and group similar records in a dataset. FindMatches can also generate a primary key for each group of records and remove duplicates. FindMatches does not require any coding or prior ML experience, as it can learn from a sample of labeled data provided by the user. FindMatches can also scale to handle large datasets and optimize the cost and performance of the ETL job. Reference:
AWS Glue
FindMatches ML Transform
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
질문 # 57
......
꿈을 안고 사는 인생이 멋진 인생입니다. 고객님의 최근의 꿈은 승진이나 연봉인상이 아닐가 싶습니다. Amazon인증 Data-Engineer-Associate시험은 IT인증시험중 가장 인기있는 국제승인 자격증을 취득하는데서의 필수시험과목입니다.그만큼 시험문제가 어려워 시험도전할 용기가 없다구요? 이제 이런 걱정은 버리셔도 됩니다. Pass4Test의 Amazon인증 Data-Engineer-Associate덤프는Amazon인증 Data-Engineer-Associate시험에 대비한 공부자료로서 시험적중율 100%입니다.
Data-Engineer-Associate높은 통과율 시험자료: 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=11xoxQbR2jpLUCtAZyeVhzJntXIS01vQA