그 외, DumpTOP Data-Engineer-Associate 시험 문제집 일부가 지금은 무료입니다: https://drive.google.com/open?id=1B5FneDl7CGYqpaIZYsgp1EHokE96GmW3
우리DumpTOP 사이트에서Amazon Data-Engineer-Associate관련자료의 일부 문제와 답 등 샘플을 제공함으로 여러분은 무료로 다운받아 체험해보실 수 있습니다.체험 후 우리의DumpTOP에 신뢰감을 느끼게 됩니다.빨리 우리 DumpTOP의 덤프를 만나보세요.
| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Implement data quality and validation - Transform and enrich data
|
| Data Security and Governance | 18% | - Encrypt data at rest and in transit - Implement access control and authentication
- Enforce compliance and data governance
|
| Data Store Management | 26% | - Design and implement data storage solutions
- Manage data lifecycle and storage tiers |
| Data Operations and Support | 22% | - Ensure reliability and scalability - Backup, restore, and disaster recovery - Monitor and troubleshoot data pipelines
|
>> Data-Engineer-Associate최신버전 시험덤프공부 <<
Amazon 인증Data-Engineer-Associate시험에 도전해보려고 하는데 공부할 내용이 너무 많아 스트레스를 받는 분들은 지금 보고계시는 공부자료는 책장에 다시 넣으시고DumpTOP의Amazon 인증Data-Engineer-Associate덤프자료에 주목하세요. DumpTOP의 Amazon 인증Data-Engineer-Associate덤프는 오로지 Amazon 인증Data-Engineer-Associate시험에 대비하여 제작된 시험공부가이드로서 시험패스율이 100%입니다. 시험에서 떨어지면 덤프비용전액환불해드립니다.
질문 # 89
A company is creating a new data pipeline to populate a data lake. A data analyst needs to prepare and standardize the data before a data engineering team can perform advanced data transformations. The data analyst needs a solution to process the data that does not require writing new code.
Which solution will meet these requirements with the LEAST operational effort?
정답：D
설명：
Option C best matches the requirement of no new code with least operational effort because it keeps the analyst's work inside the AWS-native, visual ETL experience and produces standardized outputs that data engineers can extend. The study material emphasizes that AWS provides visual data preparation capabilities that let users "clean and normalize data without writing any code," which is exactly what the analyst needs before advanced engineering transformations begin.
Option A requires Python and Pandas, which directly violates the "does not require writing new code" requirement and introduces dependency management and debugging overhead. Option B uses multiple services (Canvas + Data Wrangler + Glue), which increases the number of moving parts, permissions, handoffs, and operational surface area compared to a single-service preparation approach. Option D offloads the entire preparation step to engineers, which increases operational effort and delays because the analyst cannot directly implement and iterate on standardization.
Using recipe-style transformations in the Glue visual interface aligns with the documented goal of simplifying data preparation workflows while enabling the engineering team to add more complex steps later in the pipeline.
질문 # 90
A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables have primary keys. Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?
정답：A
설명：
The company needs to ingest tables from an on-premises Oracle database into a data lake on Amazon S3 in Apache Parquet format. The most efficient solution, requiring the least manual effort, would be to use AWS Database Migration Service (DMS) for continuous data replication.
* Option C: Create an AWS Database Migration Service (AWS DMS) task for ongoing replication.
Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.AWS DMS can continuously replicate data from the Oracle database into Amazon S3, transforming it into Parquet format as it ingests the data. DMS simplifies the process by providing ongoing replication with minimal setup, and it automatically handles the conversion to Parquet format without requiring manual transformations or separate jobs. This option is the least effort solution since it automates both the ingestion and transformation processes.
Other options:
* Option A (Apache Sqoop on EMR) involves more manual configuration and management, including setting up EMR clusters and writing Sqoop jobs.
* Option B (AWS Glue bookmark job) involves configuring Glue jobs, which adds complexity. While Glue supports data transformations, DMS offers a more seamless solution for database replication.
* Option D (RDS and Lambda triggers) introduces unnecessary complexity by involving RDS and Lambda for a task that DMS can handle more efficiently.
References:
* AWS Database Migration Service (DMS)
* DMS S3 Target Documentation
질문 # 91
A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.
The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.
Which solution will meet these requirements with the LEAST operational overhead?
정답：C
설명：
AWS Glue workflows are a feature of AWS Glue that enable you to create and visualize complex ETL pipelines using AWS Glue components, such as crawlers, jobs, triggers, and development endpoints. AWS Glue workflows provide automated orchestration and require minimal manual effort, as they handle dependency resolution, error handling, state management, and resource allocation for your ETL workflows.
You can use AWS Glue workflows to ingest data from your operational databases into your Amazon S3 based data lake, and then use AWS Glue and Amazon EMR to process the data in the data lake. This solution will meet the requirements with the least operational overhead, as it leverages the serverless and fully managed nature of AWS Glue, and the scalability and flexibility of Amazon EMR12.
The other options are not optimal for the following reasons:
* B. AWS Step Functions tasks. AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. You can use AWS Step Functions tasks to invoke AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use AWS Step Functions state machines to define the logic and flow of your workflows. However, this option would require more manual effort than AWS Glue workflows, as you would need to write JSON code to define your state machines, handle errors and retries, and monitor the execution history and status of your workflows3.
* C. AWS Lambda functions. AWS Lambda is a service that lets you run code without provisioning or managing servers. You can use AWS Lambda functions to trigger AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use AWS Lambda event sources and destinations to orchestrate the flow of your workflows. However, this option would also require more manual effort than AWS Glue workflows, as you would need to write code to implement your business logic, handle errors and retries, and monitor the invocation and execution of your Lambda functions. Moreover, AWS Lambda functions have limitations on the execution time, memory, and concurrency, which may affect the performance and scalability of your ETL workflows.
* D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows. Amazon MWAA is a managed service that makes it easy to run open source Apache Airflow on AWS. Apache Airflow is a popular tool for creating and managing complex ETL pipelines using directed acyclic graphs (DAGs).
You can use Amazon MWAA workflows to orchestrate AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use the Airflow web interface to visualize and monitor your workflows.
However, this option would have more operational overhead than AWS Glue workflows, as you would need to set up and configure your Amazon MWAA environment, write Python code to define your DAGs, and manage the dependencies and versions of your Airflow plugins and operators.
References:
* 1: AWS Glue Workflows
* 2: AWS Glue and Amazon EMR
* 3: AWS Step Functions
* : AWS Lambda
* : Amazon Managed Workflows for Apache Airflow
질문 # 92
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 # 93
A data engineer uses the AWS Glue Data Catalog to manage data lake metadata. The data engineer ' s extract, transform, and load (ETL) process creates new partitions in an Amazon S3 data lake throughout the day. The new partitions are not queryable through Amazon Athena until an AWS Glue crawler run finishes each night.
The data engineer needs to make new partitions immediately available for querying.
Which solution will meet these requirements?
정답：D
설명：
The correct answer is A because Athena can query new partitions only after the partitions exist in the AWS Glue Data Catalog. Waiting for a nightly crawler creates unnecessary delay. The direct fix is to update the Data Catalog as part of the ETL process by calling the AWS Glue CreatePartition API immediately after each new S3 partition is written. AWS Glue documentation states that CreatePartition creates a new partition and requires the database name, table name, and partition input structure. Copying data to another bucket does not register partitions. DynamoDB Streams is unrelated unless the partition lifecycle is already stored in DynamoDB. StartImportLabelsTaskRun is not the API for partition registration. The correct operational pattern is event/write partition, then immediately register metadata.
질문 # 94
......
DumpTOP는 여러분을 성공으로 가는 길에 도움을 드리는 사이트입니다. DumpTOP에서는 여러분이 안전하게 간단하게Amazon인증Data-Engineer-Associate시험을 패스할 수 있는 자료들을 제공함으로 빠른 시일 내에 IT관련지식을 터득하고 한번에 시험을 패스하실 수 있습니다.
Data-Engineer-Associate인증시험대비 덤프공부: https://www.dumptop.com/Amazon/Data-Engineer-Associate-dump.html
참고: DumpTOP에서 Google Drive로 공유하는 무료, 최신 Data-Engineer-Associate 시험 문제집이 있습니다: https://drive.google.com/open?id=1B5FneDl7CGYqpaIZYsgp1EHokE96GmW3