Amazon Data-Engineer-Associate模試エンジン、Data-Engineer-Associate資格関連題

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Security and Governance	18%	- Implement data security controls - Apply governance and compliance best practices
Topic 2: Data Store Management	26%	- Optimize storage performance and cost - Select appropriate data storage solutions
Topic 3: Data Ingestion and Transformation	34%	- Build and manage data pipelines - Ingest and transform data using AWS services
Topic 4: Data Operations and Support	22%	- Monitor and maintain data pipelines - Troubleshoot data workflow issues

>> Amazon Data-Engineer-Associate模試エンジン <<

Data-Engineer-Associate資格関連題 & Data-Engineer-Associateファンデーション

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) 認定 Data-Engineer-Associate 試験問題 (Q221-Q226):

質問 # 221
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?

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

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 # 222
A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.
The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.
Which solution will meet these requirements?

A. Attach an IAM role to the EKS worker nodes. Grant the IAM role access to DynamoDB. Use the IAM role to set up IAM roles service accounts (IRSA) functionality.
B. Create an IAM user that has an access key to access the DynamoDB table. Use Kubernetes secrets that are mounted in a volume of the EKS cluster nodes to store the user access key data.
C. Create an IAM user that has an access key to access the DynamoDB table. Use environment variables in the EKS containers to store the IAM user access key data.
D. Store the AWS credentials in an Amazon S3 bucket. Grant the EKS containers access to the S3 bucket to retrieve the credentials.

正解：A

解説：
In this scenario, the company is using Amazon Elastic Kubernetes Service (EKS) and wants secure access to DynamoDB without embedding credentials inside the application containers. The best practice is to useIAM roles for service accounts (IRSA), which allows assigning IAM roles to Kubernetes service accounts. This lets the EKS pods assume specific IAM roles securely, without the need to store credentials in containers.
* IAM Roles for Service Accounts (IRSA):
* With IRSA, each pod in the EKS cluster can assume an IAM role that grants access to DynamoDB without needing to manage long-term credentials. The IAM role can be attached to the service account associated with the pod.
* This ensuresleast privilegeaccess, improving security by preventing credentials from being embedded in the containers.
Reference:IAM Roles for Service Accounts (IRSA)
Alternatives Considered:
A (Storing AWS credentials in S3): Storing AWS credentials in S3 and retrieving them introduces security risks and violates the principle of not embedding credentials.
C (IAM user access keys in environment variables): This also embeds credentials, which is not recommended.
D (Kubernetes secrets): Storing user access keys as secrets is an option, but it still involves handling long- term credentials manually, which is less secure than using IRSA.
References:
IAM Best Practices for Amazon EKS
Secure Access to DynamoDB from EKS

質問 # 223
A company stores its processed data in an S3 bucket. The company has a strict data access policy. The company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.
The company wants to receive notifications when a user violates the data access policy. Each notification must include the username of the user who violated the policy.
Which solution will meet these requirements?

A. Use Amazon CloudWatch metrics to gather object-level metrics. Set up CloudWatch alarms.
B. Use Amazon S3 server access logs to monitor access to the bucket. Forward the access logs to an Amazon CloudWatch log group. Use metric filters on the log group to set up CloudWatch alarms.
C. Use AWS Config rules to detect violations of the data access policy. Set up compliance alarms.
D. Use AWS CloudTrail to track object-level events for the S3 bucket. Forward events to Amazon CloudWatch to set up CloudWatch alarms.

正解：D

解説：
The requirement is to detect violations of data access policies and receive notifications with the username of the violator. AWS CloudTrail can provideobject-level tracking for S3to capture detailed API actions on specific S3 objects, including the user who performed the action.
* AWS CloudTrail:
* CloudTrail can monitorAPI callsmade to an S3 bucket, including object-level API actions such as GetObject, PutObject, and DeleteObject. This will help detect access violations based on the API calls made by different users.
* CloudTrail logs include details such as theuser identity, which is essential for meeting the requirement of including the username in notifications.
* The CloudTrail logs can be forwarded toAmazon CloudWatchto trigger alarms based on certain access patterns (e.g., violations of specific policies).
Reference:Monitoring Amazon S3 Activity Using AWS CloudTrail
Amazon CloudWatch:
By forwarding CloudTrail logs to CloudWatch, you can set upalarmsthat are triggered when a specific condition is met, such as unauthorized access or policy violations. The alarm can include detailed information from the CloudTrail log, including the username.
Alternatives Considered:
A (AWS Config rules): While AWS Config can track resource configurations and compliance, it does not provide real-time, detailed tracking of object-level events like CloudTrail does.
B (CloudWatch metrics): CloudWatch does not gather object-level metrics for S3 directly. For this use case, CloudTrail provides better granularity.
D (S3 server access logs): S3 server access logs can monitor access, but they do not provide the real-time monitoring and alerting features that CloudTrail with CloudWatch alarms offer. They also do not include API- level granularity like CloudTrail.
References:
AWS CloudTrail Integration with S3
Amazon CloudWatch Alarms

質問 # 224
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.
Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.
Which factors could have caused the reordering system to receive duplicated data? (Select TWO.)

A. The AggregationEnabled configuration property was set to true.
B. The stream's value for the IteratorAgeMilliseconds metric was too high.
C. The max_records configuration property was set to a number that was too high.
D. The producer experienced network-related timeouts.
E. There was a change in the number of shards, record processors, or both.

正解：D、E

解説：
* Problem Analysis:
* The company usesKinesis Data Streamsfor both inventory management and reordering.
* TheKinesis Producer Library (KPL)publishes data, and theKinesis Client Library (KCL) consumes data.
* Duplicate records were observed in the inventory reordering system.
* Key Considerations:
* Kinesis streams are designed for durability but may produce duplicates under certain conditions.
* Factors such asnetwork timeouts,shard splits, or changes inrecord processorscan cause duplication.
* Solution Analysis:
* Option A: Network-Related Timeouts
* If the producer (KPL) experiences network timeouts, it retries data submission, potentially causing duplicates.
* Option B: High IteratorAgeMilliseconds
* High iterator age suggests delays in processing but does not directly cause duplication.
* Option C: Changes in Shards or Processors
* Changes in the number of shards or record processors can lead to re-processing of records, causing duplication.
* Option D: AggregationEnabled Set to True
* AggregationEnabled controls the aggregation of multiple records into one, but it does not cause duplication.
* Option E: High max_records Value
* A high max_records value increases batch size but does not lead to duplication.
* Final Recommendation:
* Network-related timeoutsandchanges in shards or processorsare the most likely causes of duplicate data in this scenario.
:
Amazon Kinesis Data Streams Best Practices
Kinesis Producer Library (KPL) Overview
Kinesis Client Library (KCL) Overview

質問 # 225
A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day.
The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket.
The company runs a daily report on the S3 data. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic.
Which solution will meet this requirement with the LEAST operational overhead?

A. Create AWS Lambda functions that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow that runs the Lambda functions. Configure the Step Functions workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
B. Create data quality checks on the source datasets that the daily reports use. Create a new Amazon EMR cluster. Use Apache Spark SQL to create Apache Spark jobs in the EMR cluster that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow. Configure the workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
C. Create data quality checks for the source datasets that the daily reports use. Create a new AWS managed Apache Airflow cluster. Run the data quality checks by using Airflow tasks that run data quality queries on the columns data type and the presence of nullvalues. Configure Airflow Directed Acyclic Graphs (DAGs) to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
D. Create data quality checks on the source datasets that the daily reports use. Create data quality actions by using AWS Glue workflows to confirm the completeness and consistency of the datasets. Configure the data quality actions to create an event in Amazon EventBridge if a dataset is incomplete. Configure EventBridge to send the event that informs the data engineer about the incomplete datasets to the Amazon SNS topic.

正解：D

解説：
AWS Glue workflows are designed to orchestrate the ETL pipeline, and you can createdata quality checksto ensure the uploaded datasets are complete before running reports. If there is an issue with the data, AWS Glue workflows can trigger anAmazon EventBridge eventthat sends a message to anSNS topic.
* AWS Glue Workflows:
* AWS Glue workflows allow users to automate and monitor complex ETL processes. You can includedata quality actionsto check for null values, data types, and other consistency checks.
* In the event of incomplete data, anEventBridgeevent can be generated to notify via SNS.
Reference:AWS Glue Workflows
Alternatives Considered:
A (Airflow cluster): Managed Airflow introduces more operational overhead and complexity compared to Glue workflows.
B (EMR cluster): Setting up an EMR cluster is also more complex compared to the Glue-centric solution.
D (Lambda functions): While Lambda functions can work, using Glue workflows offers a more integrated and lower operational overhead solution.
References:
AWS Glue Workflow Documentation

質問 # 226
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Data-Engineer-Associate学習ガイドを選択することは、学習コンテンツの充実だけでなく、独自の発見スペースを改善する機会でもあります。当社のData-Engineer-Associate学習ガイド資料は、あなたの個人的な開発に大きな影響を与える可能性があります。仕事を探している過程で、競合他社よりも有利なData-Engineer-Associate証明書を保持しているため、君は。 Data-Engineer-Associate学習ガイド資料を使用した後、ユーザーは専攻に専念するためにより多くの時間とエネルギーを費やすことができ、専門分野でますます目立つようになります。

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