P.S.JPTestKingがGoogle Driveで共有している無料の2026 Amazon Data-Engineer-Associateダンプ：https://drive.google.com/open?id=1gX75x9SUYN9jfX8dNJPeLpU03CuDTndS
最近、Amazonの認定試験はますます人気があるようになっています。それと同時に、Amazonの認証資格ももっと重要になっています。IT業界では広く認可されている試験として、Data-Engineer-Associate認定試験はAmazonの中の最も重要な試験の一つです。この試験の認証資格を取ったら、あなたは多くの利益を得ることができます。あなたもこの試験を受ける予定があれば、JPTestKingのData-Engineer-Associate問題集は試験に準備するときに欠くことができないツールです。この問題集はData-Engineer-Associate認定試験に関連する最も優秀な参考書ですから。
| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Choose a data store
|
| Data Ingestion and Transformation | 34% | - Perform data ingestion
|
| Data Security and Governance | 18% | - Apply authentication and authorization
|
| Data Operations and Support | 22% | - Monitor data pipelines
|
>> Data-Engineer-Associate対策学習 <<
AmazonのData-Engineer-Associate認定試験に受かることを悩んでいたら、JPTestKingを選びましょう。JPTestKingのAmazonのData-Engineer-Associate試験トレーニング資料は間違いなく最高のトレーニング資料ですから、それを選ぶことはあなたにとって最高の選択です。IT専門家になりたいですか。そうだったら、JPTestKingを利用したください。
質問 # 289
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications? (Select TWO.)
正解：C、E
解説：
Amazon Neptune supports graph applications using Gremlin and SPARQL as query languages. Neptune is a fully managed graph database service that supports both property graph and RDF graph models.
* Option A: GremlinGremlin is a query language for property graph databases, which is supported by Amazon Neptune. It allows the traversal and manipulation of graph data in the property graph model.
* Option D: SPARQLSPARQL is a query language for querying RDF graph data in Neptune. It is used to query, manipulate, and retrieve information stored in RDF format.
Other options:
* SQL (Option B) and ANSI SQL (Option C) are traditional relational database query languages and are not used for graph databases.
* Spark SQL (Option E) is related to Apache Spark for big data processing, not for querying graph databases.
References:
* Amazon Neptune Documentation
* Gremlin Documentation
* SPARQL Documentation
質問 # 290
A global finance company needs to implement near real-time cross-Region synchronization of trading data between trading centers in the us-east-1 Region, the eu-west-2 Region, and the ap-northeast-1 Region. The company must ensure that data is encrypted in transit. The solution must ensure data ordering and consistency and must support cross-Region disaster recovery. The solution must provide data latency of less than 500 milliseconds.
Which solution will meet these requirements with the LEAST operational effort?
正解：C
解説：
Option B is correct because Amazon MSK Replicator is the AWS-managed feature specifically designed to reliably replicate data across Amazon MSK clusters in different AWS Regions. AWS states that MSK Replicator can be used to build regionally resilient streaming applications for availability and business continuity, which directly supports the disaster recovery requirement. It is also the choice with the least operational effort because it avoids deploying and managing self-hosted replication tools such as Kafka Connect or MirrorMaker 2. AWS documentation for cross-Region setup also shows support for secure connectivity and notes that MSK environments support IAM, TLS, and SASL/SCRAM authentication schemes for private connectivity scenarios.
Option A and C would require the company to deploy, scale, patch, and monitor custom replication infrastructure in every Region, which increases operational overhead. Option D is not appropriate because Kinesis Data Firehose is not the native service for replicating data between MSK clusters. For ordering and consistency, Kafka-based replication preserves partitioned stream semantics better than mixing in a different streaming service. Therefore, a mesh of MSK Replicator relationships between regional MSK clusters is the best AWS-native answer for near real-time, encrypted, cross-Region replication with minimal administration.
質問 # 291
A company needs to load customer data that comes from a third party into an Amazon Redshift data warehouse. The company stores order data and product data in the same data warehouse. The company wants to use the combined dataset to identify potential new customers.
A data engineer notices that one of the fields in the source data includes values that are in JSON format.
How should the data engineer load the JSON data into the data warehouse with the LEAST effort?
正解：A
解説：
In Amazon Redshift, the SUPER data type is designed specifically to handle semi-structured data like JSON, Parquet, ORC, and others. By using the SUPER data type, Redshift can ingest and query JSON data without requiring complex data flattening processes, thus reducing the amount of preprocessing required before loading the data. The SUPER data type also works seamlessly with Redshift Spectrum, enabling complex queries that can combine both structured and semi-structured datasets, which aligns with the company's need to use combined datasets to identify potential new customers.
Using the SUPER data type also allows for automatic parsing and query processing of nested data structures through Amazon Redshift's PARTITION BY and JSONPATH expressions, which makes this option the most efficient approach with the least effort involved. This reduces the overhead associated with using tools like AWS Glue or Lambda for data transformation.
References:
* Amazon Redshift Documentation - SUPER Data Type
* AWS Certified Data Engineer - Associate Training: Building Batch Data Analytics Solutions on AWS
* AWS Certified Data Engineer - Associate Study Guide
By directly leveraging the capabilities of Redshift with the SUPER data type, the data engineer ensures streamlined JSON ingestion with minimal effort while maintaining query efficiency.
質問 # 292
A company wants to ingest streaming data into an Amazon Redshift data warehouse from an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. A data engineer needs to develop a solution that provides low data access time and that optimizes storage costs.
Which solution will meet these requirements with the LEAST operational overhead?
正解：A
解説：
According to the guide:
"For integrating streaming data from Amazon MSK into Amazon Redshift efficiently and cost-effectively, AWS Glue streaming jobs can process and transform the data, storing it in Amazon S3. Amazon Redshift Spectrum can then directly query the data from S3, minimizing operational overhead and reducing storage costs."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This setup offers:
* Low latencyvia Glue streaming.
* Low storage costby using Parquet/ORC on S3.
* Minimal operational overheadby avoiding complex pipelines or constantly updated materialized views.
質問 # 293
A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisionedcapacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.
Which solution will meet these requirements with the LEAST operational overhead?
正解：A
解説：
The best solution to meet the requirements of giving data scientists the ability to query all data sources by using syntax similar to SQL with the least operational overhead is to use AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use Amazon Athena to query the data, use SQL for structured data sources, and use PartiQL for data that is stored in JSON format.
AWS Glue is a serverless data integration service that makes it easy to prepare, clean, enrich, and move data between data stores1. 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. The Data Catalog is a persistent metadata store that contains table definitions, job definitions, and other control information to help you manage your AWS Glue components3. You can use AWS Glue to crawl the data sources, such as Amazon S3, Amazon RDS for Microsoft SQL Server, and Amazon DynamoDB, and store the metadata in the Data Catalog.
Amazon Athena is a serverless, interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL or Python4. Amazon Athena also supports PartiQL, a SQL-compatible query language that lets you query, insert, update, and delete data from semi-structured and nested data, such as JSON. You can use Amazon Athena to query the data from the Data Catalog using SQL for structured data sources, such as .csv files and relational databases, and PartiQL for data that is stored in JSON format. You can also use Athena to query data from other data sources, such as Amazon Redshift, using federated queries.
Using AWS Glue and Amazon Athena to query all data sources by using syntax similar to SQL is the least operational overhead solution, as you do not need to provision, manage, or scale any infrastructure, and you pay only for the resources you use. AWS Glue charges you based on the compute time and the data processed by your crawlers and ETL jobs1. Amazon Athena charges you based on the amount of data scanned by your queries. You can also reduce the cost and improve the performance of your queries by using compression, partitioning, and columnar formats for your data in Amazon S3.
Option B is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, and use Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena. Redshift Spectrum is a feature of Amazon Redshift, a fully managed data warehouse service, that allows you to query and join data across your data warehouse and your data lake using standard SQL. While Redshift Spectrum is powerful and useful for many data warehousing scenarios, it is not necessary or cost-effective for querying all data sources by using syntax similar to SQL. Redshift Spectrum charges you based on the amount of data scanned by your queries, which is similar to Amazon Athena, but it also requires you to have an Amazon Redshift cluster, which charges you based on the node type, the number of nodes, and the duration of the cluster5. These costs can add up quickly, especially if you have large volumes of data and complex queries. Moreover, using Redshift Spectrum would introduce additional latency and complexity, as you would have to provision and manage the cluster, and create an external schema and database for the data in the Data Catalog, instead of querying it directly from Amazon Athena.
Option C is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format, store the transformed data in an S3 bucket, and use Amazon Athena to query the original and transformed data from the S3 bucket, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Glue jobs are ETL scripts that you can write in Python or Scala to transform your data and load it to your target data store. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes6. While using AWS Glue jobs and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to write, run, and monitor the ETL jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using AWS Glue jobs and Parquet would introduce additional latency, as you would have to wait for the ETL jobs to finish before querying the transformed data.
Option D is not the best solution, as using AWS Lake Formation to create a data lake, use Lake Formation jobs to transform the data from all data sources to Apache Parquet format, store the transformed data in an S3 bucket, and use Amazon Athena or RedshiftSpectrum to query the data, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Lake Formation is a service that helps you centrally govern, secure, and globally share data for analytics and machine learning7. Lake Formation jobs are ETL jobs that you can create and run using the Lake Formation console or API. While using Lake Formation and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to create, run, and monitor the Lake Formation jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using Lake Formation and Parquet would introduce additional latency, as you would have to wait for the Lake Formation jobs to finish before querying the transformed data. Furthermore, using Redshift Spectrum to query the data would also incur the same costs and complexity as mentioned in option B. References:
What is Amazon Athena?
Data Catalog and crawlers in AWS Glue
AWS Glue Data Catalog
Columnar Storage Formats
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
AWS Glue Schema Registry
What is AWS Glue?
Amazon Redshift Serverless
Amazon Redshift provisioned clusters
[Querying external data using Amazon Redshift Spectrum]
[Using stored procedures in Amazon Redshift]
[What is AWS Lambda?]
[PartiQL for Amazon Athena]
[Federated queries in Amazon Athena]
[Amazon Athena pricing]
[Top 10 performance tuning tips for Amazon Athena]
[AWS Glue ETL jobs]
[AWS Lake Formation jobs]
質問 # 294
......
Data-Engineer-Associate準備急流はタイミング機能を高め、内容は理解しやすく、重要な情報を簡素化しました。 Data-Engineer-Associateテストブレインダンプは、より重要な情報をより少ない回答と質問で伝え、学習をリラックスして効率的にします。試験に不合格になった場合は、すぐに返金されます。AmazonすべてのData-Engineer-Associate試験トレントは、Data-Engineer-Associate試験に簡単かつ正常に合格するために多くの助けを与えます。 Data-Engineer-Associate試験問題を試してみてください。どれだけ優れているかがわかります。
Data-Engineer-Associate日本語認定対策: https://www.jptestking.com/Data-Engineer-Associate-exam.html
無料でクラウドストレージから最新のJPTestKing Data-Engineer-Associate PDFダンプをダウンロードする：https://drive.google.com/open?id=1gX75x9SUYN9jfX8dNJPeLpU03CuDTndS