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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section Weight Objectives
Topic 1: Data Store Management 26% - Design data models
  • 1. Partitioning and indexing strategies
  • 2. Normalization and denormalization
  • 3. Schema design
- Choose a data store
  • 1. Data lakes vs. data warehouses
  • 2. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift
  • 3. Data characteristics (structured, semi-structured, unstructured)
  • 4. Access and storage patterns
- Manage data lifecycle
  • 1. Amazon S3 storage classes
  • 2. Data retention policies
  • 3. Data archiving
- Understand data cataloging
  • 1. Schema evolution
  • 2. AWS Glue Data Catalog
  • 3. Data discovery and classification
Topic 2: Data Security and Governance 18% - Manage data privacy and compliance
  • 1. AWS Lake Formation permissions
  • 2. Data masking and tokenization
  • 3. PII data handling
- Implement data quality checks
  • 1. AWS Glue DataBrew
  • 2. Data validation
- Ensure data encryption
  • 1. Encryption at rest and in transit
  • 2. AWS KMS
- Apply authentication and authorization
  • 1. AWS IAM policies and roles
  • 2. Service control policies (SCPs)
  • 3. Amazon S3 bucket policies
Topic 3: Data Operations and Support 22% - Automate data pipelines
  • 1. Scheduling jobs
  • 2. Event-driven triggers
  • 3. AWS Lambda triggers
- Manage and troubleshoot data processes
  • 1. Performance tuning
  • 2. Debugging failed jobs
  • 3. Cost optimization
- Monitor data pipelines
  • 1. AWS CloudTrail
  • 2. Amazon CloudWatch
  • 3. Logging and metrics
Topic 4: Data Ingestion and Transformation 34% - Transform and process data
  • 1. Batch and stream processing
  • 2. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda)
  • 3. ETL/ELT patterns
  • 4. Data partitioning and compression
- Perform data ingestion
  • 1. Throughput and latency characteristics for AWS services
  • 2. Replayability of data
  • 3. Streaming data ingestion
  • 4. Data ingestion patterns (frequency and data history)
  • 5. Batch data ingestion (scheduled ingestion, event-driven ingestion)
- Orchestrate data pipelines
  • 1. Amazon Managed Workflows for Apache Airflow (MWAA)
  • 2. Event-driven architectures
  • 3. AWS Glue Workflows
  • 4. AWS Step Functions
- Apply programming concepts
  • 1. Infrastructure as Code (IaC)
  • 2. Version control
  • 3. SQL, Python, Scala

>> Data-Engineer-Associate最新試験 <<

Amazon Data-Engineer-Associate Exam | Data-Engineer-Associate最新試験 - パスを助ける Data-Engineer-Associate: AWS Certified Data Engineer - Associate (DEA-C01) 試験

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) 認定 Data-Engineer-Associate 試験問題 (Q264-Q269):

質問 # 264
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?

  • A. Change the format of the files that are in the dataset to Apache Parquet.
  • B. Use the query result reuse feature of Amazon Athena for the SQL queries.
  • C. Add an Amazon ElastiCache cluster between the Bl application and Athena.
  • D. Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day

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 # 265
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?

  • A. Schedule an AWS Glue crawler to run every morning.
  • B. Manually run the AWS Glue CreatePartition API twice each day.
  • C. Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create partition API call.
  • D. Run the MSCK REPAIR TABLE command from the AWS Glue console.

正解：A

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 run continuously 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 # 266
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?

  • A. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
  • B. Use the Amazon Redshift Data API to publish an event to Amazon EventBridqe. Configure an EventBridge rule to invoke the Lambda function.
  • C. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
  • D. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.

正解：A

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 # 267
A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.
The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.
Which combination of steps will meet this requirement with the LEAST operational overhead? (Choose two.)

  • A. Configure the Lambda function to run in the same subnet that the DB instance uses.
  • B. Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.
  • C. Turn on the public access setting for the DB instance.
  • D. Attach the same security group to the Lambda function and the DB instance. Include a self-referencing rule that allows access through the database port.
  • E. Update the security group of the DB instance to allow only Lambda function invocations on the database port.

正解：A、D

解説：
To enable the Lambda function to connect to the RDS DB instance privately without using the public internet, the best combination of steps is to configure the Lambda function to run in the same subnet that the DB instance uses, and attach the same security group to the Lambda function and the DB instance. This way, the Lambda function and the DB instance can communicate within the same private network, and the security group can allow traffic between them on the database port. This solution has the least operational overhead, as it does not require any changes to the public access setting, the network ACL, or the security group of the DB instance.
The other options are not optimal for the following reasons:
* A. Turn on the public access setting for the DB instance. This option is not recommended, as it would expose the DB instance to the public internet, which can compromise the security and privacy of the data. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.
* B. Update the security group of the DB instance to allow only Lambda function invocations on the database port. This option is not sufficient, as it would only modify the inbound rules of the security group of the DB instance, but not the outbound rules of the security group of the Lambda function.
Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.
* E. Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port. This option is not necessary, as the network ACL of the private subnet already allows all traffic within the subnet by default. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.
:
1: Connecting to an Amazon RDS DB instance
2: Configuring a Lambda function to access resources in a VPC
3: Working with security groups
4: Network ACLs


質問 # 268
A company uses an Amazon S3 bucket to integrate multiple data sources into a central data lake. The company needs to perform multiple transformations and data cleaning processes on the data to make the data accessible to business partners.
The company needs a solution that will give multiple business partners the ability to run SQL queries on the central data lake during normal business hours.
Which solution will meet these requirements MOST cost-effectively?

  • A. Use an AWS Lambda function after normal business hours to process the previous day's data, apply all necessary transformations, and load the prepared data into an Amazon Redshift provisioned cluster.
  • B. Use a provisioned Amazon EMR cluster after normal business hours to process the previous day's data, apply all necessary transformations, and load the prepared data into Amazon Redshift Serverless.
  • C. Use an AWS Glue Flex job after normal business hours to process the previous day's data, apply all necessary transformations, and load the prepared data into Amazon Redshift Serverless.
  • D. Use an AWS Glue Flex job after normal business hours to process the previous day's data, apply all necessary transformations, and load the prepared data into an Amazon Redshift provisioned cluster.

正解：C

解説：
Option B is most cost-effective because it combines a serverless ETL service for overnight processing with a serverless warehouse for partner SQL access during business hours. The material describes AWS Glue as a
"fully managed, serverless, scalable ETL service" that can extract, transform, and load data with minimal operational overhead, which fits the requirement for multiple transformations and data cleaning.
For the SQL access layer, the study material highlights the cost advantage of Amazon Redshift Serverless: it automatically provisions and scales capacity and you pay only for the compute capacity provisioned, with no compute costs when no workloads are running. That directly supports "multiple business partners" querying during business hours while avoiding the always-on cost of a provisioned cluster.
Options C and D use a provisioned Redshift cluster, which is less cost-effective because capacity must be paid for even when partners are not querying. Option A adds EMR cluster management cost and operational overhead compared to using Glue for ETL.


質問 # 269
......

Data-Engineer-Associate模擬テストは、シラバスの変更とAmazon理論と実践の最新の進展に応じて何百人もの専門家によって改訂された高品質の製品であり、各学生が重要なコンテンツの学習を完了することができるように焦点を絞ってターゲットを絞っています 最短時間で。 Data-Engineer-Associateトレーニング準備では、Data-Engineer-Associate試験を受ける前に20〜30時間の練習をするだけで済みます。 一方、Data-Engineer-Associate試験の質問を使用すると、AWS Certified Data Engineer - Associate (DEA-C01)試験の焦点が失われることを心配する必要はありません。

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P.S.GoShiken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=16TFqL5CNngSoxdK4TlWrTA_tXnkrdtWr

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