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GoShikenのAmazonのData-Engineer-Associate試験のトレーニングキットはGoShikenのIT技術専門家たちによって開発されたのです。そのデザインは当面の急速に変化するIT市場と密接な関係があります。GoShikenのトレーニングはあなたを助けて継続的に発展している技術を利用して、問題を解決する能力を高めると同時に仕事についての満足度を向上させることができます。GoShikenのAmazonのData-Engineer-Associateの認証したカバー率は１００パーセントに達したのですから、弊社の問題と解答を利用する限り、あなたがきっと気楽に試験に合格することを保証します。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Topic 2: Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Topic 3: Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Topic 4: Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
>> Data-Engineer-Associate日本語関連対策 <<
もし君はAmazonのData-Engineer-Associate認定試験に合格するのを通じて、競争が激しいIT業種での地位を高めて、IT技能を増強するなら、GoShikenの AmazonのData-Engineer-Associate試験トレーニング資料を選んだほうがいいです。長年の努力を通じて、GoShikenのAmazonのData-Engineer-Associate認定試験の合格率が１００パーセントになっていました。GoShikenを選ぶのは成功を選ぶのに等しいです。
質問 # 157
A company processes 500 GB of audience and advertising data daily, storing CSV files in Amazon S3 with schemas registered in AWS Glue Data Catalog. They need to convert these files to Apache Parquet format and store them in an S3 bucket.
The solution requires a long-running workflow with 15 GiB memory capacity to process the data concurrently, followed by a correlation process that begins only after the first two processes complete.
正解：D
解説：
AWS Glue Workflows can coordinate multiple ETL jobs and triggers. They support parallel execution and sequential dependencies, which is ideal for concurrent data processing followed by correlation steps, all with minimal operational overhead.
"Use AWS Glue Workflows to orchestrate multiple ETL jobs in sequence or in parallel, supporting conditional triggers and dependency management."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
質問 # 158
A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format: s3://bucket/prefix/year=2023/month=01/day=01.
A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.
Which solution will meet these requirements with the LEAST latency?
正解：D
解説：
The best solution to ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket with the least latency is to use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create partition API call. This way, the Data Catalog is updated as soon as new data is written to S3, and the partition information is immediately available for querying by other services. The Boto3 AWS Glue create partition API call allows you to create a new partition in the Data Catalog by specifying the table name, the database name, and the partition values1. You can use this API call in your code that writes data to S3, such as a Python script or an AWS Glue ETL job, to create a partition for each new S3 object key that matches the partitioning scheme.
Option A is not the best solution, as scheduling an AWS Glue crawler to run every morning would introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. AWS Glue crawlers are processes that connect to a data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in the Data Catalog2. Crawlers can be scheduled to run periodically, such as daily or hourly, but they cannot runcontinuously or in real-time.
Therefore, using a crawler to synchronize the Data Catalog with the S3 storage would not meet the requirement of the least latency.
Option B is not the best solution, as manually running the AWS Glue CreatePartition API twice each day would also introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. Moreover, manually running the API would require more operational overhead and human intervention than using code that writes data to S3 to invoke the API automatically.
Option D is not the best solution, as running the MSCK REPAIR TABLE command from the AWS Glue console would also introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. The MSCK REPAIR TABLE command is a SQL command that you can run in the AWS Glue console to add partitions to the Data Catalog based on the S3 object keys that match the partitioning scheme3. However, this command is not meant to be run frequently or in real-time, as it can take a long time to scan the entire S3 bucket and add the partitions. Therefore, using this command to synchronize the Data Catalog with the S3 storage would not meet the requirement of the least latency. References:
AWS Glue CreatePartition API
Populating the AWS Glue Data Catalog
MSCK REPAIR TABLE Command
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
質問 # 159
A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.
The company needs to identify matching records even when the records do not have a common unique identifier.
Which solution will meet this requirement?
正解：D
解説：
The problem described requires identifying matching records even when there is no unique identifier. AWS Lake Formation FindMatches is designed for this purpose. It uses machine learning (ML) to deduplicate and find matching records in datasets that do not share a common identifier.
* D. Train and use the AWS Lake Formation FindMatches transform in the ETL job:
* FindMatches is a transform available in AWS Lake Formation that uses ML to discover duplicate records or related records that might not have a common unique identifier.
* It can be integrated into an AWS Glue ETL job to perform deduplication or matching tasks.
* FindMatches is highly effective in scenarios where records do not share a key, such as customer records from different sources that need to be merged or reconciled.
質問 # 160
A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?
正解：C
解説：
To create a new table named cities_usa in Amazon Athena based on a subset of data from the existing cities_world table, you should use an INSERT INTO statement combined with a SELECT statement to filter only the records where the country is 'usa'. The correct SQL syntax would be:
Option A: INSERT INTO cities_usa (city, state) SELECT city, state FROM cities_world WHERE country='usa';This statement inserts only the cities and states where the country column has a value of 'usa' from the cities_world table into the cities_usa table. This is a correct approach to create a new table with data filtered from an existing table in Athena.
Options B, C, and D are incorrect due to syntax errors or incorrect SQL usage (e.g., the MOVE command or the use of UPDATE in a non-relevant context).
References:
Amazon Athena SQL Reference
Creating Tables in Athena
質問 # 161
A data engineer is configuring Amazon SageMaker Studio to use AWS Glue interactive sessions to prepare data for machine learning (ML) models.
The data engineer receives an access denied error when the data engineer tries to prepare the data by using SageMaker Studio.
Which change should the engineer make to gain access to SageMaker Studio?
正解：C
解説：
This solution meets the requirement of gaining access to SageMaker Studio to use AWS Glue interactive sessions. AWS Glue interactive sessions are a way to use AWS Glue DataBrew and AWS Glue Data Catalog from within SageMaker Studio. To use AWS Glue interactive sessions, the data engineer's IAM user needs to have permissions to assume the AWS Glue service role and the SageMaker execution role. By adding a policy to the data engineer's IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy, the data engineer can grant these permissions and avoid the access denied error. The other options are not sufficient or necessary to resolve the error. References:
Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions Troubleshoot Errors - Amazon SageMaker AccessDeniedException on sagemaker:CreateDomain in AWS SageMaker Studio, despite having SageMakerFullAccess
質問 # 162
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GoShiken世界は急速に変化しており、従業員に対する要件はこれまでになく高くなっています。理想的な仕事を見つけて高収入を得たい場合は、優れた労働能力と深いAmazon知識を高めなければなりません。 Data-Engineer-AssociateのAWS Certified Data Engineer - Associate (DEA-C01)認定に合格すると、夢を実現できます。製品を購入すると、最高のAWS Certified Data Engineer - Associate (DEA-C01)学習教材が提供され、AWS Certified Data Engineer - Associate (DEA-C01)認定の取得にData-Engineer-Associate役立ちます。当社の製品は高品質であり、当社のサービスは完璧です。
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