ちなみに、JPTestKing Data-Engineer-Associateの一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1gX75x9SUYN9jfX8dNJPeLpU03CuDTndS
ここ数年、Data-Engineer-Associate復習教材は、無数の受験者がData-Engineer-Associate試験に合格するのに役立ちました。Data-Engineer-Associate認定資格証明書を取得した後、仕事機会が増え、偉大な企業家になり、専門家になった人もいました。Data-Engineer-Associate復習教材は多くのいい評価をもらいました。良い評判で、Data-Engineer-Associate復習教材を選択する人がますます増えています。
| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Orchestrate data pipelines
|
| Data Store Management | 26% | - Manage data lifecycle
|
| Data Security and Governance | 18% | - Implement data quality checks
|
| Data Operations and Support | 22% | - Manage and troubleshoot data processes
|
>> Data-Engineer-Associate試験合格攻略 <<
時代に対応するために、科学技術は人々の学習方法を向上させると信じています。特にこのようなペースの速い生活テンポでは、効率の高い学習を非常に重視しています。したがって、当社のData-Engineer-Associate学習資料は、過去の試験問題と現在の試験の傾向に基づいており、実際の試験環境に配置するためのこのような効果的なシミュレーション機能を設計します。高度なData-Engineer-Associate学習教材を備えた高品質のシミュレーションシステムを提供することをお約束します。シミュレーション機能により、Data-Engineer-Associateトレーニングガイドの理解が容易になり、Data-Engineer-Associate試験に合格できます。
質問 # 249
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：B
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 # 250
A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.
The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to useData Quality Definition Language (DQDL)rules in an AWS Glue Data Quality job.
Which rule will meet these requirements?
正解：A
解説：
TheDatasetMatchrule in DQDL checks for full value equivalence between mapped fields. A value of1.0 indicates a100% match. The correct syntax and metric for an exact match scenario are:
"Use DatasetMatch when comparing mapped fields between two datasets. The comparison score of 1.0 confirms a perfect match."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Options with "100" use incorrect syntax since DQDL usesfloating-point scores(e.g., 1.0, 0.95), not percentages.
質問 # 251
A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.
A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.
The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?
正解：A
解説：
To achieve the most cost-effective storage solution, the data engineer needs to use an S3 Lifecycle policy that transitions objects to lower-cost storage classes based on their access patterns, and deletes them when they are no longer needed. The storage classes should also provide high availability, which means they should be resilient to the loss of data in a single Availability Zone1. Therefore, the solution must include the following steps:
* Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is designed for data that is accessed less frequently, but requires rapid access when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost and a retrieval fee2. Therefore, it is suitable for data files that are accessed once or twice each month. S3 Standard-IA also provides high availability, as it stores data redundantly across multiple Availability Zones1.
* Transfer objects to S3 Glacier Deep Archive after 2 years. S3 Glacier Deep Archive is the lowest-cost storage class that offers secure and durable storage for data that is rarely accessed and can tolerate a 12- hour retrieval time. It is ideal for long-term archiving and digital preservation3. Therefore, it is suitable for data files that are accessed only once or twice each year. S3 Glacier Deep Archive also provides high availability, as it stores data across at least three geographically dispersed Availability Zones1.
* Delete objects when they are no longer needed. The data engineer can specify an expiration action in the S3 Lifecycle policy to delete objects after a certain period of time. This will reduce the storage cost and comply with any data retention policies.
Option C is the only solution that includes all these steps. Therefore, option C is the correct answer.
Option A is incorrect because it transitions objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after
6 months. S3 One Zone-IA is similar to S3 Standard-IA, but it stores data in a single Availability Zone. This means it has a lower availability and durability than S3 Standard-IA, and it is not resilient to the loss of data in a single Availability Zone1. Therefore, it does not provide high availability as required.
Option B is incorrect because it transfers objects to S3 Glacier Flexible Retrieval after 2 years. S3 Glacier Flexible Retrieval is a storage class that offers secure and durable storage for data that is accessed infrequently and can tolerate a retrieval time of minutes to hours. It is more expensive than S3 Glacier Deep Archive, and it is not suitable for data that is accessed only once or twice each year3. Therefore, it is not the most cost-effective option.
Option D is incorrect because it combines the errors of option A and B. It transitions objects to S3 One Zone- IA after 6 months, which does not provide high availability, and it transfers objects to S3 Glacier Flexible Retrieval after 2 years, which is not the most cost-effective option.
References:
* 1: Amazon S3 storage classes - Amazon Simple Storage Service
* 2: Amazon S3 Standard-Infrequent Access (S3 Standard-IA) - Amazon Simple Storage Service
* 3: Amazon S3 Glacier and S3 Glacier Deep Archive - Amazon Simple Storage Service
* [4]: Expiring objects - Amazon Simple Storage Service
* [5]: Managing your storage lifecycle - Amazon Simple Storage Service
* [6]: Examples of S3 Lifecycle configuration - Amazon Simple Storage Service
* [7]: Amazon S3 Lifecycle further optimizes storage cost savings with new features - What's New with AWS
質問 # 252
A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?
正解：B
解説：
The best solution to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs is to use the query result reuse feature of Amazon Athena for the SQL queries. This feature allows you to run the same query multiple times without incurring additional charges, as long as the underlying data has not changed and the query results are still in the query result location in Amazon S31. This feature is useful for scenarios where you have a petabyte-scale dataset that is updated infrequently, such as once a day, and you have a BI application that runs the same queries repeatedly, such as every hour. By using the query result reuse feature, you can reduce the amount of data scanned by your queries and save on the cost of running Athena. You can enable or disable this feature at the workgroup level or at the individual query level1.
Option A is not the best solution, as configuring an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day would not cost optimize the company's use of Amazon Athena, but rather increase the cost and complexity. Amazon S3 Lifecycle policies are rules that you can define to automatically transition objects between different storage classes based on specified criteria, such as the age of the object2. S3 Glacier Deep Archive is the lowest-cost storage class in Amazon S3, designed for long-term data archiving that is accessed once or twice in a year3. While moving data to S3 Glacier Deep Archive can reduce the storage cost, it would also increase the retrieval cost and latency, as it takes up to 12 hours to restore the data from S3 Glacier Deep Archive3. Moreover, Athena does not support querying data that is in S3 Glacier or S3 Glacier Deep Archive storage classes4. Therefore, using this option would not meet the requirements of running on-demand SQL queries on the dataset.
Option C is not the best solution, as adding an Amazon ElastiCache cluster between the BI application and Athena would not cost optimize the company's use of Amazon Athena, but rather increase the cost and complexity. Amazon ElastiCache is a service that offers fully managed in-memory data stores, such as Redis and Memcached, that can improve the performance and scalability of web applications by caching frequently accessed data. While using ElastiCache can reduce the latency and load on the BI application, it would not reduce the amount of data scanned by Athena, which is the main factor that determines the cost of running Athena. Moreover, using ElastiCache would introduce additional infrastructure costs and operational overhead, as you would have to provision, manage, and scale the ElastiCache cluster, and integrate it with the BI application and Athena.
Option D is not the best solution, as changing the format of the files that are in the dataset to Apache Parquet would not cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs, but rather increase the complexity. 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 schemes. However, changing the format of the files that are in the dataset to Apache Parquet would require additional processing and transformation steps, such as using AWS Glue or Amazon EMR to convert the files from their original format to Parquet, and storing the converted files in a separate location in Amazon S3. This would increase the complexity and the operational overhead of the data pipeline, and also incur additional costs for using AWS Glue or Amazon EMR. Reference:
Query result reuse
Amazon S3 Lifecycle
S3 Glacier Deep Archive
Storage classes supported by Athena
[What is Amazon ElastiCache?]
[Amazon Athena pricing]
[Columnar Storage Formats]
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
質問 # 253
A data engineer manages a provisioned Amazon Redshift cluster that stores petabytes of data. The cluster receives continuous data ingestion from multiple sources. As data volume grows, the query performance has degraded. The current table design uses the AUTO distribution style and default sort keys. The data engineer needs to optimize the query performance for recent transactions.
Which solution will meet these requirements?
正解：B
解説：
The correct answer is A because the workload has grown to petabyte scale, but the performance requirement is focused on recent transactions. The best architecture is to keep hot, frequently queried data inside Amazon Redshift and move historical/cold data to Amazon S3. Amazon Redshift Spectrum lets Redshift efficiently query structured and semi-structured files in S3 without loading that data into Redshift tables, and most of the data remains in S3 while Spectrum performs parallel processing. This reduces pressure on the provisioned cluster while preserving query access to historical data. Decreasing nodes would worsen performance. DISTSTYLE ALL for historical tables can increase storage and maintenance overhead. Redshift is already columnar, so switching to row-based storage is not valid.
質問 # 254
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