順便提一下，可以從雲存儲中下載Fast2test Data-Engineer-Associate考試題庫的完整版：https://drive.google.com/open?id=1RDVIL98DSmK-GG9w07lYbXgI9sHCMIos
我們Fast2test Amazon的Data-Engineer-Associate考題按照相同的教學大綱，其次是實際的Data-Engineer-Associate認證考試，我們也在不斷升級我們的培訓資料，使你在第一時間得到最好和最新的資訊。當你購買我們Data-Engineer-Associate的考試培訓材料，你所得到的培訓資料有長達一年的免費更新期，你可以隨時延長更新訂閱時間，讓你有更久的時間來準備考試。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion and Transformation | 34% | - Ingest data from various sources
- Transform and enrich data
|
| Topic 2: Data Operations and Support | 22% | - Backup, restore, and disaster recovery - Ensure reliability and scalability - Monitor and troubleshoot data pipelines
|
| Topic 3: Data Store Management | 26% | - Optimize storage performance and cost - Design and implement data storage solutions
|
| Topic 4: Data Security and Governance | 18% | - Enforce compliance and data governance
- Protect sensitive data |
>> 新版Data-Engineer-Associate題庫上線 <<
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問題 #200
A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.
The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.
A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.
Which solution will meet these requirements with the LEAST operational effort?
答案：B
解題說明：
Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers the least operational effort by avoiding complex data movement and transformation processes.
Amazon Athena Federated Queries:
Athena's federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena's support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.
The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.
Reference:
Alternatives Considered:
A (AWS Glue pipeline): This would work but requires more operational effort to manage and transform the data in AWS Glue.
C (Amazon EMR): Using EMR and writing PySpark code introduces more operational overhead and complexity compared to a SQL-based solution in Athena.
D (Amazon AppFlow): AppFlow is more suitable for transferring data between services but is not as efficient for transformations and joins as Athena federated queries.
Amazon Athena Documentation
Federated Queries in Amazon Athena
問題 #201
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?
答案：C
解題說明：
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
問題 #202
A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables have primary keys. Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?
答案：B
解題說明：
The company needs to ingest tables from an on-premises Oracle database into a data lake on Amazon S3 in Apache Parquet format. The most efficient solution, requiring the least manual effort, would be to use AWS Database Migration Service (DMS) for continuous data replication.
* Option C: Create an AWS Database Migration Service (AWS DMS) task for ongoing replication.
Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.AWS DMS can continuously replicate data from the Oracle database into Amazon S3, transforming it into Parquet format as it ingests the data. DMS simplifies the process by providing ongoing replication with minimal setup, and it automatically handles the conversion to Parquet format without requiring manual transformations or separate jobs. This option is the least effort solution since it automates both the ingestion and transformation processes.
Other options:
* Option A (Apache Sqoop on EMR) involves more manual configuration and management, including setting up EMR clusters and writing Sqoop jobs.
* Option B (AWS Glue bookmark job) involves configuring Glue jobs, which adds complexity. While Glue supports data transformations, DMS offers a more seamless solution for database replication.
* Option D (RDS and Lambda triggers) introduces unnecessary complexity by involving RDS and Lambda for a task that DMS can handle more efficiently.
References:
* AWS Database Migration Service (DMS)
* DMS S3 Target Documentation
問題 #203
A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.
Which AWS service or feature will meet these requirements with the LEAST operational overhead?
答案：B
解題說明：
Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3 and AmazonRedshift. Amazon AppFlow supports many SaaS applications as data sources and targets, and allows you to configure data flows with a few clicks.
Amazon AppFlow also provides features such as data transformation, filtering, validation, and encryption to prepare and protect your data. Amazon AppFlow meets the requirements of the media company with the least operational overhead, as it eliminates the need to write code, manage infrastructure, or monitor data pipelines.
References:
Amazon AppFlow
Amazon AppFlow | SaaS Integrations List
Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions
問題 #204
A company is uploading log files from on-premises servers to an Amazon S3 bucket. The company needs to validate that the logs from the on-premises servers are the same as the logs that are stored in the S3 bucket.
Which solution will meet this requirement?
答案：C
解題說明：
Amazon S3 natively supports checksum validation during uploads when objects are uploaded by using the AWS SDK. The SDK can automatically calculate a checksum, such as CRC32, on the client side and send it with the upload request. Amazon S3 validates the checksum upon receipt and stores it with the object, ensuring that the data uploaded is exactly the same as the source data.
Storing the checksum in the S3 object metadata provides a lightweight and cost-effective way to verify data integrity without requiring additional compute, storage, or post-processing steps. This approach validates the logs at upload time, which is the most efficient and reliable method.
Using AWS Lambda to calculate SHA-256 checksums would require additional compute, orchestration, and storage, increasing cost and operational complexity. Amazon S3 Object Lock enforces immutability and retention but does not validate data integrity between source and destination. Governance or compliance mode Object Lock cannot detect whether uploaded data matches the original logs.
Therefore, using the AWS SDK checksum validation during upload is the simplest, most efficient, and AWS- recommended approach for ensuring log integrity.
問題 #205
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