Amazon Data-Engineer-Associate인증덤프공부, Data-Engineer-Associate인기공부자료

Amazon Data-Engineer-Associate 덤프가 고객님의 기대를 가득 채워드릴수 있도록 정말로 노력하고 있는 Itcertkr랍니다. Amazon Data-Engineer-Associate 덤프는 pdf버전과 소프트웨어버전으로만 되어있었는데 최근에는 휴대폰에서가 사용가능한 온라인버전까지 개발하였습니다. 날따라 새로운 시스템을 많이 개발하여 고객님께 더욱 편하게 다가갈수 있는 Itcertkr가 되겠습니다.

Itcertkr의Amazon Data-Engineer-Associate 덤프 구매 후 등록된 사용자가 구매일로부터 일년 이내에Amazon Data-Engineer-Associate시험에 실패하셨다면 Itcertkr메일에 주문번호와 불합격성적표를 보내오셔서 환불신청하실수 있습니다.구매일자 이전에 발생한 시험불합격은 환불보상의 대상이 아닙니다. 개별 인증사는 불합격성적표를 발급하지 않기에 재시험신청내역을 환불증명으로 제출하시면 됩니다.

>> Amazon Data-Engineer-Associate인증덤프공부 <<

Amazon Data-Engineer-Associate인기공부자료 & Data-Engineer-Associate퍼펙트 최신버전 문제

Amazon인증 Data-Engineer-Associate시험을 한방에 편하게 통과하여 자격증을 취득하려면 시험전 공부가이드가 필수입니다. Itcertkr에서 연구제작한 Amazon인증 Data-Engineer-Associate덤프는Amazon인증 Data-Engineer-Associate시험을 패스하는데 가장 좋은 시험준비 공부자료입니다. Itcertkr덤프공부자료는 엘리트한 IT전문자들이 자신의 노하우와 경험으로 최선을 다해 연구제작한 결과물입니다.IT인증자격증을 취득하려는 분들의 곁은Itcertkr가 지켜드립니다.

최신 AWS Certified Data Engineer Data-Engineer-Associate 무료샘플문제 (Q151-Q156):

질문 # 151
A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Which solution will meet this requirement MOST cost-effectively?

A. Use Amazon Athena Federated Query to join the data from all data sources.
B. Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.
C. Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.
D. Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.

정답：A

설명：
Amazon Athena Federated Query is a feature that allows you to query data from multiple sources using standard SQL. You can use Athena Federated Query to join data from Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3, as well as other data sources such as MongoDB, Apache HBase, and Apache Kafka1. Athena Federated Query is a serverless and interactive service, meaning you do not need to provision or manage any infrastructure, and you only pay for the amount of data scanned by your queries.
Athena Federated Query is the most cost-effective solution for performing a one-time analysis job on data from multiple sources, as it eliminates the need to copy or move data, and allows you to query data directly from the source.
The other options are not as cost-effective as Athena Federated Query, as they involve additional steps or costs. Option A requires you to provision and pay for an Amazon EMR cluster, which can be expensive and time-consuming for a one-time job. Option B requires you to copy or move data from DynamoDB, RDS, and Redshift to S3, which can incur additional costs for data transfer and storage, and also introduce latency and complexity. Option D requires you to have an existing Redshift cluster, which can be costly and may not be necessary for a one-time job. Option D also does not supportquerying data from RDS directly, so you would need to use Redshift Federated Query to access RDS data, which adds another layer of complexity2.
References:
Amazon Athena Federated Query
Redshift Spectrum vs Federated Query

질문 # 152
A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.
A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.
The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?

A. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
B. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
C. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
D. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.

정답：B

설명：
To achieve the most cost-effective storage solution, the data engineer needs to use an S3 Lifecycle policy that transitions objects to lower-cost storage classes based on their access patterns, and deletes them when they are no longer needed. The storage classes should also provide high availability, which means they should be resilient to the loss of data in a single Availability Zone1. Therefore, the solution must include the following steps:
Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is designed for data that is accessed less frequently, but requires rapid access when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost and a retrieval fee2. Therefore, it is suitable for data files that are accessed once or twice each month. S3 Standard-IA also provides high availability, as it stores data redundantly across multiple Availability Zones1.
Transfer objects to S3 Glacier Deep Archive after 2 years. S3 Glacier Deep Archive is the lowest-cost storage class that offers secure and durable storage for data that is rarely accessed and can tolerate a 12-hour retrieval time. It is ideal for long-term archiving and digital preservation3. Therefore, it is suitable for data files that are accessed only once or twice each year. S3 Glacier Deep Archive also provides high availability, as it stores data across at least three geographically dispersed Availability Zones1.
Delete objects when they are no longer needed. The data engineer can specify an expiration action in the S3 Lifecycle policy to delete objects after a certain period of time. This will reduce the storage cost and comply with any data retention policies.
Option C is the only solution that includes all these steps. Therefore, option C is the correct answer.
Option A is incorrect because it transitions objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. S3 One Zone-IA is similar to S3 Standard-IA, but it stores data in a single Availability Zone. This means it has a lower availability and durability than S3 Standard-IA, and it is not resilient to the loss of data in a single Availability Zone1. Therefore, it does not provide high availability as required.
Option B is incorrect because it transfers objects to S3 Glacier Flexible Retrieval after 2 years. S3 Glacier Flexible Retrieval is a storage class that offers secure and durable storage for data that is accessed infrequently and can tolerate a retrieval time of minutes to hours. It is more expensive than S3 Glacier Deep Archive, and it is not suitable for data that is accessed only once or twice each year3. Therefore, it is not the most cost-effective option.
Option D is incorrect because it combines the errors of option A and B. It transitions objects to S3 One Zone-IA after 6 months, which does not provide high availability, and it transfers objects to S3 Glacier Flexible Retrieval after 2 years, which is not the most cost-effective option.
Reference:
1: Amazon S3 storage classes - Amazon Simple Storage Service
2: Amazon S3 Standard-Infrequent Access (S3 Standard-IA) - Amazon Simple Storage Service
3: Amazon S3 Glacier and S3 Glacier Deep Archive - Amazon Simple Storage Service
[4]: Expiring objects - Amazon Simple Storage Service
[5]: Managing your storage lifecycle - Amazon Simple Storage Service
[6]: Examples of S3 Lifecycle configuration - Amazon Simple Storage Service
[7]: Amazon S3 Lifecycle further optimizes storage cost savings with new features - What's New with AWS

질문 # 153
A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution.
A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations.
The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.
Which solution will meet these requirements?

A. Upgrade the reserved node from ra3.4xlarqe to ra3.16xlarqe.
B. Change the distribution key to the table column that has the largest dimension.
C. Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
D. Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.

정답：B

설명：
Changing the distribution key to the table column that has the largest dimension will help to balance the load more evenly across all five compute nodes. The distribution key determines how the rows of a table are distributed among the slices of the cluster. If the distribution key is not chosen wisely, it can cause data skew, meaning some slices will have more data than others, resulting in uneven CPU load and query performance.
By choosing the table column that has the largest dimension, meaning the column that has the most distinct values, as the distribution key, the data engineer can ensure that the rows are distributed more uniformly across the slices, reducing data skew and improving query performance.
The other options are not solutions that will meet the requirements. Option A, changing the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load. The sort key determines the order in which the rows of a table are stored on disk, which can improve the performance of range-restricted queries, but not the load balancing. Option C, upgrading the reserved node from ra3.4xlarge to ra3.16xlarge, will not maintain the current number of compute nodes, as it will increase the cost and the capacity of the cluster. Option D, changing the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load either. The primary key is a constraint that enforces the uniqueness of the rows in a table, but it does not influence the data layout or the query optimization. References:
Choosing a data distribution style
Choosing a data sort key
Working with primary keys

질문 # 154
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

A. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.
B. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
C. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
D. Use the Amazon Redshift Data API to publish an event to Amazon EventBridqe. Configure an EventBridge rule to invoke the Lambda function.

정답：C

설명：
The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. Reference:
Using the Amazon Redshift Data API
Using Amazon EventBridge with Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift

질문 # 155
A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.
A data engineer must implement a solution that can detect the schema for these data sources. The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.
B. Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.
C. Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.
D. Create a PvSpark proqram in AWS Lambda to extract, transform, and load the data into the S3 bucket.

정답：A

설명：
AWS Glue is a fully managed service that provides a serverless data integration platform. It can automatically discover and categorize data from various sources, including SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. It can also infer the schema of the data and store it in the AWS Glue Data Catalog, which is a central metadata repository. AWS Glue can then use the schema information to generate and run Apache Spark code to extract, transform, and load the data into an Amazon S3 bucket. AWS Glue can also monitor and optimize the performance and cost of the data pipeline, and handle any schema changes that may occur in the source data. AWS Glue can meet the SLA of loading the data into the S3 bucket within 15 minutes of data creation, as it can trigger the data pipeline based on events, schedules, or on-demand. AWS Glue has the least operational overhead among the options, as it does not require provisioning, configuring, or managing any servers or clusters. It also handles scaling, patching, and security automatically. Reference:
AWS Glue
[AWS Glue Data Catalog]
[AWS Glue Developer Guide]
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

질문 # 156
......

Itcertkr Amazon인증Data-Engineer-Associate시험덤프 구매전 구매사이트에서 무료샘플을 다운받아 PDF버전 덤프내용을 우선 체험해보실수 있습니다. 무료샘플을 보시면Itcertkr Amazon인증Data-Engineer-Associate시험대비자료에 믿음이 갈것입니다.고객님의 이익을 보장해드리기 위하여Itcertkr는 시험불합격시 덤프비용전액환불을 무조건 약속합니다. Itcertkr의 도움으로 더욱 많은 분들이 멋진 IT전문가로 거듭나기를 바라는바입니다.

Data-Engineer-Associate인기공부자료: https://www.itcertkr.com/Data-Engineer-Associate_exam.html

여러분은 우리Itcertkr 사이트에서 제공하는Amazon Data-Engineer-Associate관련자료의 일부분문제와답등 샘플을 무료로 다운받아 체험해볼 수 있습니다, Data-Engineer-Associate덤프의 세가지 버전중 한가지 버전만 구매하셔도 되고 세가지 버전을 패키지로 구매하셔도 됩니다, 쉽게 시험을 통과하시려는 분께 Data-Engineer-Associate덤프를 추천해드립니다, Itcertkr 표 Amazon인증Data-Engineer-Associate덤프는 시험출제 예상문제를 정리해둔 실제시험문제에 가장 가까운 시험준비공부자료로서 공을 들이지않고도 시험패스가 가능합니다, 이건 모두 Itcertkr Data-Engineer-Associate인기공부자료 인증시험덤프로 공부하였기 때문입니다, Data-Engineer-Associate덤프는 오랜 시간과 정력을 투자하여 만들어낸 완벽한 시험자료로서 Data-Engineer-Associate덤프를 구매하고 공부하였는데도 시험에서 떨어졌다면 불합격성적표와 주문번호를 보내오시면 Data-Engineer-Associate덤프비용은 바로 환불해드립니다.

준영이 세은의 때아닌 아재개그에 피식 웃었지만, 그녀는 웃지Data-Engineer-Associate않았다, 고깔모자를 쓰고 피리 나팔을 불며 파티하는 모습, 유아용 장난감을 가지고 노는 모습, 코에 생크림을 묻히고 케이크를 만드는 모습 등, 여러분은 우리Itcertkr 사이트에서 제공하는Amazon Data-Engineer-Associate관련자료의 일부분문제와답등 샘플을 무료로 다운받아 체험해볼 수 있습니다.

100% 유효한 Data-Engineer-Associate인증덤프공부 덤프공부

Data-Engineer-Associate덤프의 세가지 버전중 한가지 버전만 구매하셔도 되고 세가지 버전을 패키지로 구매하셔도 됩니다, 쉽게 시험을 통과하시려는 분께 Data-Engineer-Associate덤프를 추천해드립니다, Itcertkr 표 Amazon인증Data-Engineer-Associate덤프는 시험출제 예상문제를 정리해둔 실제시험문제에 가장 가까운 시험준비공부자료로서 공을 들이지않고도 시험패스가 가능합니다.

이건 모두 Itcertkr 인증시험덤프로 공부하였기 때문입니다.

Victor Jones Victor Jones

Biography

Amazon Data-Engineer-Associate인증덤프공부, Data-Engineer-Associate인기공부자료

Amazon Data-Engineer-Associate인기공부자료 & Data-Engineer-Associate퍼펙트 최신버전 문제

최신 AWS Certified Data Engineer Data-Engineer-Associate 무료샘플문제 (Q151-Q156):

100% 유효한 Data-Engineer-Associate인증덤프공부 덤프공부

Subscribe

All Access Membership