さらに、JPNTest Data-Engineer-Associateダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=13SujtZIEksS3sKGTFBFf-lPHILeTsXWo
自分の幸せは自分で作るものだと思われます。ただ、社会に入るIT卒業生たちは自分能力の不足で、Data-Engineer-Associate試験向けの仕事を探すのを悩んでいますか？それでは、弊社のAmazonのData-Engineer-Associate練習問題を選んで実用能力を速く高め、自分を充実させます。その結果、自信になる自己は面接のときに、面接官のいろいろな質問を気軽に回答できて、順調にData-Engineer-Associate向けの会社に入ります。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion and Transformation | 34% | - Transform and process data
|
| Topic 2: Data Store Management | 26% | - Choose a data store
|
| Topic 3: Data Operations and Support | 22% | - Monitor data pipelines
|
| Topic 4: Data Security and Governance | 18% | - Implement data quality checks
|
>> Data-Engineer-Associate日本語認定 <<
IT認定試験の中でどんな試験を受けても、JPNTestのData-Engineer-Associate試験参考資料はあなたに大きなヘルプを与えることができます。それは JPNTestのData-Engineer-Associate問題集には実際の試験に出題される可能性がある問題をすべて含んでいて、しかもあなたをよりよく問題を理解させるように詳しい解析を与えますから。真剣にJPNTestのAmazon Data-Engineer-Associate問題集を勉強する限り、受験したい試験に楽に合格することができるということです。
質問 # 251
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：C
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.
1: AWS Glue Workflows
2: AWS Glue and Amazon EMR
3: AWS Step Functions
AWS Lambda
Amazon Managed Workflows for Apache Airflow
質問 # 252
A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real- time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?
正解：A
解説：
The Amazon Redshift Data API is a built-in feature that allows you to run SQL queries on Amazon Redshift data with web services-based applications, such as AWS Lambda, Amazon SageMaker notebooks, and AWS Cloud9. The Data API does not require a persistent connection to your database, and it provides a secure HTTP endpoint and integration with AWS SDKs. You can use the endpoint to run SQL statements without managing connections. The Data API also supports both Amazon Redshift provisioned clusters and Redshift Serverless workgroups. The Data API is the best solution for running real-time queries on the financial data from within the trading application, as it has the least operational overhead compared to the other options.
Option A is not the best solution, as establishing WebSocket connections to Amazon Redshift would require more configuration and maintenance than using the Data API. WebSocket connections are also not supported by Amazon Redshift clusters or serverless workgroups.
Option C is not the best solution, as setting up JDBC connections to Amazon Redshift would also require more configuration and maintenance than using the Data API. JDBC connections are also not supported by Redshift Serverless workgroups.
Option D is not the best solution, as storing frequently accessed data in Amazon S3 and using Amazon S3 Select to run the queries would introduce additional latency and complexity than using the Data API. Amazon S3 Select is also not optimized for real-time queries, as it scans the entire object before returning the results. References:
Using the Amazon Redshift Data API
Calling the Data API
Amazon Redshift Data API Reference
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
質問 # 253
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?
正解：D
解説：
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. References:
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
質問 # 254
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 # 255
A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer's original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data
WHERE year = 2023
GROUP BY product_name
How should the data engineer modify the Athena query to meet these requirements?
正解：D
解説：
The original query does not return results for all of the products because the year column in the sales_data table is not an integer, but a timestamp. Therefore, the WHERE clause does not filter the data correctly, and only returns the products that have a null value for the year column. To fix this, the data engineer should use the extract function to extract the year from the timestamp and compare it with 2023. This way, the querywill return the correct results for all of the products in the sales_data table. The other options are either incorrect or irrelevant, as they do not address the root cause of the issue. Replacing sum with count does not change the filtering condition, adding HAVING clause does not affect the grouping logic, and removing the GROUP BY clause does not solve the problem of missing products. References:
Troubleshooting JSON queries - Amazon Athena (Section: JSON related errors) When I query a table in Amazon Athena, the TIMESTAMP result is empty (Section: Resolution) AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 7, page 197)
質問 # 256
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JPNTestは客様の要求を満たせていい評判をうけいたします。たくさんのひとは弊社の商品を使って、Data-Engineer-Associate試験に順調に合格しました。
Data-Engineer-Associate難易度: https://www.jpntest.com/shiken/Data-Engineer-Associate-mondaishu
さらに、JPNTest Data-Engineer-Associateダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=13SujtZIEksS3sKGTFBFf-lPHILeTsXWo