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Data-Engineer-Associate시험덤프샘플최신기출문제공부하기

Itexamdump에는 전문적인 업계인사들이Amazon Data-Engineer-Associate시험문제와 답에 대하여 연구하여, 시험준비중인 여러분들한테 유용하고 필요한 시험가이드를 제공합니다. 만약Itexamdump의 제품을 구매하려면, 우리Itexamdump에서는 아주 디테일 한 설명과 최신버전 최고품질의자료를 즉적중율이 높은 문제와 답을제공합니다.Amazon Data-Engineer-Associate자료는 충분한 시험대비자료가 될 것입니다. 안심하시고 Itexamdump가 제공하는 상품을 사용하시고, 100%통과 율을 확신합니다.

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>> Data-Engineer-Associate시험덤프샘플 <<

Data-Engineer-Associate시험기출문제 & Data-Engineer-Associate덤프데모문제

Itexamdump의 연구팀에서는Amazon Data-Engineer-Associate인증덤프만 위하여 지금까지 노력해왔고 Itexamdump 학습가이드Amazon Data-Engineer-Associate덤프로 시험이 어렵지 않아졌습니다. Itexamdump는 100%한번에Amazon Data-Engineer-Associate이장시험을 패스할 것을 보장하며 우리가 제공하는 문제와 답을 시험에서 백프로 나올 것입니다.여러분이Amazon Data-Engineer-Associate시험에 응시하여 우리의 도움을 받는다면 Itexamdump에서는 꼭 완벽한 자료를 드릴 것을 약속합니다. 또한 일년무료 업데이트서비스를 제공합니다.즉 문제와 답이 갱신이 되었을 경우 우리는 여러분들한테 최신버전의 문제와 답을 다시 보내드립니다.

최신 AWS Certified Data Engineer Data-Engineer-Associate 무료샘플문제 (Q171-Q176):

질문 # 171
A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB. The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)

A. Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables.
Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.
B. Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
C. Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.
D. Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
E. Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

정답：B,D

설명：
Using an Amazon EventBridge rule to run an AWS Glue job or invoke an AWS Glue workflow job every 15 minutes are two possible solutions that will meet the requirements. AWS Glue is a serverless ETL service that can process and load data from various sources to various targets, including Amazon Redshift. AWS Glue can handle different data formats, such as CSV, JSON, and Parquet, and also support schema evolution, meaning it can adapt to changes in the data schema over time. AWS Glue can also leverage Apache Spark to perform distributed processing and transformation of large datasets. AWS Glue integrates with Amazon EventBridge, which is a serverless event bus service that can trigger actions based on rules and schedules. By using an Amazon EventBridge rule, you can invoke an AWS Glue job or workflow every 15 minutes, and configure the job or workflow to run an AWS Glue crawler and then load the data into the Amazon Redshift tables. This way, you can build a cost-effective and scalable ETL pipeline that can handle data from 10 source systems and function correctly despite changes to the data schema.
The other options are not solutions that will meet the requirements. Option C, configuring an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket, and creating a second Lambda function to run the AWS Glue job, is not a feasible solution, as it would require a lot of Lambda invocations andcoordination. AWS Lambda has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the ETL pipeline. Option D, configuring an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket, is not a necessary solution, as you can use an Amazon EventBridge rule to invoke the AWS Glue workflow directly, without the need for a Lambda function. Option E, configuring an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket, and configuring the AWS Glue job to put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream, is not a cost-effective solution, as it would incur additional costs for Lambda invocations and data delivery. Moreover, using Amazon Kinesis Data Firehose to load data into Amazon Redshift is not suitable for frequent and small batches of data, as it can cause performance issues and data fragmentation. References:
AWS Glue
Amazon EventBridge
Using AWS Glue to run ETL jobs against non-native JDBC data sources
[AWS Lambda quotas]
[Amazon Kinesis Data Firehose quotas]

질문 # 172
A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB. The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)

A. Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables. Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.
B. Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
C. Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.
D. Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
E. Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

정답：B,D

설명：
Using an Amazon EventBridge rule to run an AWS Glue job or invoke an AWS Glue workflow job every 15 minutes are two possible solutions that will meet the requirements. AWS Glue is a serverless ETL service that can process and load data from various sources to various targets, including Amazon Redshift. AWS Glue can handle different data formats, such as CSV, JSON, and Parquet, and also support schema evolution, meaning it can adapt to changes in the data schema over time. AWS Glue can also leverage Apache Spark to perform distributed processing and transformation of large datasets. AWS Glue integrates with Amazon EventBridge, which is a serverless event bus service that can trigger actions based on rules and schedules. By using an Amazon EventBridge rule, you can invoke an AWS Glue job or workflow every 15 minutes, and configure the job or workflow to run an AWS Glue crawler and then load the data into the Amazon Redshift tables. This way, you can build a cost-effective and scalable ETL pipeline that can handle data from 10 source systems and function correctly despite changes to the data schema.
The other options are not solutions that will meet the requirements. Option C, configuring an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket, and creating a second Lambda function to run the AWS Glue job, is not a feasible solution, as it would require a lot of Lambda invocations and coordination. AWS Lambda has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the ETL pipeline. Option D, configuring an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket, is not a necessary solution, as you can use an Amazon EventBridge rule to invoke the AWS Glue workflow directly, without the need for a Lambda function. Option E, configuring an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket, and configuring the AWS Glue job to put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream, is not a cost-effective solution, as it would incur additional costs for Lambda invocations and data delivery. Moreover, using Amazon Kinesis Data Firehose to load data into Amazon Redshift is not suitable for frequent and small batches of data, as it can cause performance issues and data fragmentation. Reference:
AWS Glue
Amazon EventBridge
Using AWS Glue to run ETL jobs against non-native JDBC data sources
[AWS Lambda quotas]
[Amazon Kinesis Data Firehose quotas]

질문 # 173
A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets and Amazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.
Which solution will meet these requirements with the LEAST effort?

A. Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.
B. Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.
C. Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format.
D. Use scripts to scan data elements and to assign data classifications based on the format of the data.

정답：A

설명：
To build an enterprise data catalog with metadata for storage formats, the easiest and most efficient solution is using an AWS Glue crawler. The Glue crawler can scan Amazon S3 buckets and Amazon RDS databases to automatically create a data catalog that includes metadata such as the schema and storage format (e.g., CSV, Parquet, etc.). By using AWS Glue crawler classifiers, you can configure the crawler to recognize the format of the data and store this information directly in the catalog.
Option B: Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.
This option meets the requirements with the least effort because Glue crawlers automate the discovery and cataloging of data from multiple sources, including S3 and RDS, while recognizing various file formats via classifiers.
Other options (A, C, D) involve additional manual steps, like having data stewards inspect the data, or using services like Amazon Macie that focus more on sensitive data detection rather than format cataloging.
Reference:
AWS Glue Crawler Documentation
AWS Glue Classifiers

질문 # 174
A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi-node Amazon Redshift cluster. The company organizes the data files in the data lake based on the data source of each data file.
The company loads all the data files into one table in the Redshift cluster by using a separate COPY command for each data file location. This approach takes a long time to load all the data files into the table. The company must increase the speed of the data ingestion. The company does not want to increase the cost of the process.
Which solution will meet these requirements?

A. Load all the data files in parallel into Amazon Aurora. Run an AWS Glue job to load the data into Amazon Redshift.
B. Use an AWS Glue job to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.
C. Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.
D. Use a provisioned Amazon EMR cluster to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

정답：C

설명：
The company is facing performance issues loading data into Amazon Redshift because it is issuing separate COPY commands for each data file location. The most efficient way to increase the speed of data ingestion into Redshift without increasing the cost is to use a manifest file.
* Option D: Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.A manifest file provides a list of all the data files, allowing the COPY command to load all files in parallel from different locations in Amazon S3. This significantly improves the loading speed without adding costs, as it optimizes the data loading process in a single COPY operation.
Other options (A, B, C) involve additional steps that would either increase the cost (provisioning clusters, using Glue, etc.) or do not address the core issue of needing a unified and efficient COPY process.
References:
* Amazon Redshift COPY Command
* Redshift Manifest File Documentation

질문 # 175
A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for multiple applications. The company stores the logs in the data lake, catalogs the logs in AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena to analyze the logs.
Recently, customers reported that a query on one of the Athena tables did not return any dat a. A data engineer must resolve the issue.
Which combination of troubleshooting steps should the data engineer take? (Select TWO.)

A. Restart Athena.
B. Confirm that Athena is pointing to the correct Amazon S3 location.
C. Delete and recreate the problematic Athena table.
D. Increase the query timeout duration.
E. Use the MSCK REPAIR TABLE command.

정답：B,E

설명：
The problem likely arises from Athena not being able to read from the correct S3 location or missing partitions. The two most relevant troubleshooting steps involve checking the S3 location and repairing the table metadata.
A . Confirm that Athena is pointing to the correct Amazon S3 location:
One of the most common issues with missing data in Athena queries is that the query is pointed to an incorrect or outdated S3 location. Checking the S3 path ensures Athena is querying the correct data.
Reference:
C . Use the MSCK REPAIR TABLE command:
When new partitions are added to the S3 bucket without being reflected in the Glue Data Catalog, Athena queries will not return data from those partitions. The MSCK REPAIR TABLE command updates the Glue Data Catalog with the latest partitions.
Alternatives Considered:
B (Increase query timeout): Timeout issues are unrelated to missing data.
D (Restart Athena): Athena does not require restarting.
E (Delete and recreate table): This introduces unnecessary overhead when the issue can be resolved by repairing the table and confirming the S3 location.
Athena Query Fails to Return Data

질문 # 176
......

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Itexamdump는 여러분이 Amazon인증Data-Engineer-Associate시험 패스와 추후사업에 모두 도움이 되겠습니다.Itexamdump제품을 선택함으로 여러분은 시간과 돈을 절약하는 일석이조의 득을 얻을수 있습니다, Data-Engineer-Associate 인기시험덤프만 공부하시면 시험패스의 높은 산을 넘을수 있습니다, Amazon Data-Engineer-Associate덤프를 구매하시기전에 사이트에서 해당 덤프의 무료샘플을 다운받아 덤프품질을 체크해보실수 있습니다, Amazon Data-Engineer-Associate 시험자료를 찾고 계시나요, Data-Engineer-Associate 시험은 IT인사들중에서 뜨거운 인기를 누리고 있습니다, Data-Engineer-Associate 인기덤프자료 덤프구매전 데모부터 다운받아 공부해보세요.데모문제는 덤프에 포함되어 있는 문제기에 덤프품질 체크가 가능합니다.

라고 기다리는 대답을 들려주는 것도 잊지 않았다, 우우와아아아악, Itexamdump는 여러분이 Amazon인증Data-Engineer-Associate시험 패스와 추후사업에 모두 도움이 되겠습니다.Itexamdump제품을 선택함으로 여러분은 시간과 돈을 절약하는 일석이조의 득을 얻을수 있습니다.

Data-Engineer-Associate시험덤프샘플 최신 시험덤프자료

Data-Engineer-Associate 인기시험덤프만 공부하시면 시험패스의 높은 산을 넘을수 있습니다, Amazon Data-Engineer-Associate덤프를 구매하시기전에 사이트에서 해당 덤프의 무료샘플을 다운받아 덤프품질을 체크해보실수 있습니다, Amazon Data-Engineer-Associate 시험자료를 찾고 계시나요?

Data-Engineer-Associate 시험은 IT인사들중에서 뜨거운 인기를 누리고 있습니다.

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