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| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
| Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
>> Data-Engineer-Associate勉強資料 <<
CertJuken平時では、Amazon専門試験の審査に数か月から1年かかることもありますが、Data-Engineer-Associate試験ガイドを使用すれば、試験の前に20〜30時間かけて復習し、Data-Engineer-Associate学習教材を使用すれば、 Data-Engineer-Associate学習資料にはすべての重要なテストポイントが既に含まれているため、他のレビュー資料は不要になります。 同時に、Data-Engineer-Associate学習教材は、復習するためのまったく新しい学習方法を提供します-演習の過程で知識を習得しましょう。 AWS Certified Data Engineer - Associate (DEA-C01)試験に簡単かつゆっくりと合格します。
質問 # 88
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?
正解：B
解説：
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.
Reference:
1: AWS Glue Workflows
2: AWS Glue and Amazon EMR
3: AWS Step Functions
: AWS Lambda
: Amazon Managed Workflows for Apache Airflow
質問 # 89
A data engineer needs to use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket. When the data engineer connects to the QuickSight dashboard, the data engineer receives an error message that indicates insufficient permissions.
Which factors could cause to the permissions-related errors? (Choose two.)
正解：B、E
解説：
QuickSight does not have access to the S3 bucket and QuickSight does not have access to decrypt S3 data are two possible factors that could cause the permissions-related errors. Amazon QuickSight is a business intelligence service that allows you to create and share interactive dashboards based on various data sources, including Amazon Athena. Amazon Athena is a serverless query service that allows you to analyze data stored in Amazon S3 using standard SQL. To use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket, you need to grant QuickSight access to both Athena and S3, as well as any encryption keys that are used to encrypt the S3 data. If QuickSight does not have access to the S3 bucket or the encryption keys, it will not be able to read the data from Athena and display it on the dashboard, resulting in an error message that indicates insufficient permissions.
The other options are not factors that could cause the permissions-related errors. Option A, there is no connection between QuickSight and Athena, is not a factor, as QuickSight supports Athena as a native data source, and you can easily create a connection between them using the QuickSight console or the API. Option B, the Athena tables are not cataloged, is not a factor, as QuickSight can automatically discover the Athena tables that are cataloged in the AWS Glue Data Catalog, and you can also manually specify the Athena tables that are not cataloged. Option E, there is no IAM role assigned to QuickSight, is not a factor, as QuickSight requires an IAM role to access any AWS data sources, including Athena and S3, and you can create and assign an IAM role to QuickSight using the QuickSight console or the API. References:
Using Amazon Athena as a Data Source
Granting Amazon QuickSight Access to AWS Resources
Encrypting Data at Rest in Amazon S3
質問 # 90
A data engineer is building an automated extract, transform, and load (ETL) ingestion pipeline by using AWS Glue. The pipeline ingests compressed files that are in an Amazon S3 bucket. The ingestion pipeline must support incremental data processing.
Which AWS Glue feature should the data engineer use to meet this requirement?
正解：A
解説：
* Problem Analysis:
* The pipeline processescompressed filesin S3 and must supportincremental data processing.
* AWS Glue features must facilitatetracking progressto avoid reprocessing the same data.
* Key Considerations:
* Incremental data processing requires tracking which files or partitions have already been processed.
* The solution must be automated and efficient for large-scale ETL jobs.
* Solution Analysis:
* Option A: Workflows
* Workflows organize and orchestrate multiple Glue jobs but do not track progress for incremental data processing.
* Option B: Triggers
* Triggers initiate Glue jobs based on a schedule or events but do not track which data has been processed.
* Option C: Job Bookmarks
* Job bookmarks track the state of the data that has been processed, enabling incremental processing.
* Automatically skip files or partitions that were previously processed in Glue jobs.
* Option D: Classifiers
* Classifiers determine the schema of incoming data but do not handle incremental processing.
* Final Recommendation:
* Job bookmarksare specifically designed to enable incremental data processing in AWS Glue ETL pipelines.
:
AWS Glue Job Bookmarks Documentation
AWS Glue ETL Features
質問 # 91
A company receives marketing campaign data from a vendor. The company ingests the data into an Amazon S3 bucket every 40 to 60 minutes. The data is in CSV format. File sizes are between 100 KB and 300 KB.
A data engineer needs to set-up an extract, transform, and load (ETL) pipeline to upload the content of each file to Amazon Redshift.
Which solution will meet these requirements with the LEAST operational overhead?
正解：A
質問 # 92
A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.
The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog.
Which solution will meet these requirements?
正解：D
解説：
To make the S3 data accessible daily in the AWS Glue Data Catalog, the data engineer needs to create a crawler that can crawl the S3 data and write the metadata to the Data Catalog. The crawler also needs to run on a daily schedule to keep the Data Catalog updated with the latest data. Therefore, the solution must include the following steps:
* Create an IAM role that has the necessary permissions to access the S3 data and the Data Catalog. The AWSGlueServiceRole policy is a managed policy that grants these permissions1.
* Associate the role with the crawler.
* Specify the S3 bucket path of the source data as the crawler's data store. The crawler will scan the data and infer the schema and format2.
* Create a daily schedule to run the crawler. The crawler will run at the specified time every day and update the Data Catalog with any changes in the data3.
* Specify a database name for the output. The crawler will create or update a table in the Data Catalog under the specified database. The table will contain the metadata about the data in the S3 bucket, such as the location, schema, and classification.
Option B is the only solution that includes all these steps. Therefore, option B is the correct answer.
Option A is incorrect because it configures the output destination to a new path in the existing S3 bucket. This is unnecessary and may cause confusion, as the crawler does not write any data to the S3 bucket, only metadata to the Data Catalog.
Option C is incorrect because it allocates data processing units (DPUs) to run the crawler every day. This is also unnecessary, as DPUs are only used for AWS Glue ETL jobs, not crawlers.
Option D is incorrect because it combines the errors of option A and C. It configures the output destination to a new path in the existing S3 bucket and allocates DPUs to run the crawler every day, both of which are irrelevant for the crawler.
References:
* 1: AWS managed (predefined) policies for AWS Glue - AWS Glue
* 2: Data Catalog and crawlers in AWS Glue - AWS Glue
* 3: Scheduling an AWS Glue crawler - AWS Glue
* [4]: Parameters set on Data Catalog tables by crawler - AWS Glue
* [5]: AWS Glue pricing - Amazon Web Services (AWS)
質問 # 93
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「学ぶのに遅すぎることはありません」、Data-Engineer-Associate認定の準備が一般的になりつつあります。特に今日の職場では、さまざまなトレーニング資料やツールが常に混乱を招き、品質をテストする時間を無駄にしています。実際、当社のData-Engineer-Associateテスト問題を完全に信じて、Data-Engineer-Associate試験に合格することを100％保証します。 Data-Engineer-Associateのテスト問題を使用した後、残念ながら試験に不合格になった場合、証明証明書により当社から全額返金されます。
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