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試験の準備方法-便利なData-Engineer-Associateサンプル問題集試験-ユニークなData-Engineer-Associateミシュレーション問題

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Data-Engineer-Associateミシュレーション問題 & Data-Engineer-Associate合格対策

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) 認定 Data-Engineer-Associate 試験問題 (Q53-Q58):

質問 # 53
A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.
Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)

A. Use AWS Glue DataBrewto clean and prepare the data for analytics.
B. Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.
C. Configure AWS Glue triggers to run the ETL jobs even/ hour.
D. Use AWS Lambda functions to schedule and run the ETL jobs even/ hour.
E. Use the Redshift Data API to load transformed data into Amazon Redshift.

正解：B、C

解説：
The correct answer is to configure AWS Glue triggers to run the ETL jobs every hour and use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift. AWS Glue triggers are a way to schedule and orchestrate ETL jobs with the least operational overhead. AWS Glue connections are a way to securely connect to data sources and targets using JDBC or MongoDB drivers. AWS Glue DataBrew is a visual data preparation tool that does not support MongoDB as a data source. AWS Lambda functions are a serverless option to schedule and run ETL jobs, but they have a limit of 15 minutes for execution time, which may not be enough for complex transformations. The Redshift Data API is a way to run SQL commands on Amazon Redshift clusters without needing a persistent connection, but it does not support loading data from AWS Glue ETL jobs. References:
AWS Glue triggers
AWS Glue connections
AWS Glue DataBrew
[AWS Lambda functions]
[Redshift Data API]

質問 # 54
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_dat a. 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?

A. Replace sum(sales amount) with count(*J for the aggregation.
B. Change WHERE year = 2023 to WHERE extractlyear FROM sales data) = 2023.
C. Add HAVING sumfsales amount) > 0 after the GROUP BY clause.
D. Remove the GROUP BY clause

正解：B

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 query will 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. Reference:
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 # 55
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 Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.
B. 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.
C. Use scripts to scan data elements and to assign data classifications based on the format of the data.
D. 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.

正解：D

解説：
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.
References:
* AWS Glue Crawler Documentation
* AWS Glue Classifiers

質問 # 56
A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.
A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.
How should the company address the CloudWatch alarm?

A. Specify the Target-Volume-in-GiB parameter for the existing topic.
B. Expand the storage of the Apache ZooKeeper nodes.
C. Update the MSK broker instance to a larger instance type. Restart the MSK cluster.
D. Expand the storage of the MSK broker. Configure the MSK cluster storage to expand automatically.

正解：D

解説：
The RootDiskUsed metric for the MSK cluster indicates that the storage on the broker is reaching its capacity. The best solution is to expand the storage of the MSK broker and enable automatic storage expansion to prevent future alarms.
* Expand MSK Broker Storage:
* AWS Managed Streaming for Apache Kafka (MSK) allows you to expand the broker storage to accommodate growing data volumes. Additionally, auto-expansion of storage can be configured to ensure that storage grows automatically as the data increases.

質問 # 57
A data engineer maintains a materialized view that is based on an Amazon Redshift database. The view has a column named load_date that stores the date when each row was loaded.
The data engineer needs to reclaim database storage space by deleting all the rows from the materialized view.
Which command will reclaim the MOST database storage space?

A. Option D
B. Option B
C. Option C
D. Option A

正解：D

解説：
To reclaim the most storage space from a materialized view in Amazon Redshift, you should use a DELETE operation that removes all rows from the view. The most efficient way to remove all rows is to use a condition that always evaluates to true, such as 1=1. This will delete all rows without needing to evaluate each row individually based on specific column values like load_date.
Option A: DELETE FROM materialized_view_name WHERE 1=1;
This statement will delete all rows in the materialized view and free up the space. Since materialized views in Redshift store precomputed data, performing a DELETE operation will remove all stored rows.
Other options either involve inappropriate SQL statements (e.g., VACUUM in option C is used for reclaiming storage space in tables, not materialized views), or they don't remove data effectively in the context of a materialized view (e.g., TRUNCATE cannot be used directly on a materialized view).
Reference:
Amazon Redshift Materialized Views Documentation
Deleting Data from Redshift

質問 # 58
......

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