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最新的 AWS Certified Data Engineer Data-Engineer-Associate 免費考試真題:

1. A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII).
The company has an internal analytics application that does not require access to the PII.
To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset.
Which solution will meet the requirements with the LEAST operational overhead?

A) Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.
B) Use AWS Glue to transform the data for each application. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.
C) Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.
D) Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

2. A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

A) Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.
B) Turn on concurrency scaling for the daily usage quota for the Redshift cluster.
C) Turn on concurrency scaling in the settings during the creation of and new Redshift cluster.
D) Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.

3. A data engineer needs to use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket. When the data engineer connects to the QuickSight dashboard, the data engineer receives an error message that indicates insufficient permissions.
Which factors could cause to the permissions-related errors? (Choose two.)

A) QuickSiqht does not have access to the S3 bucket.
B) There is no connection between QuickSgqht and Athena.
C) The Athena tables are not cataloged.
D) QuickSight does not have access to decrypt S3 data.
E) There is no 1AM role assigned to QuickSiqht.

4. A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time.
Which solution will run the Glue jobs in the MOST cost-effective way?

A) Choose the latest version in the GlueVersion field in the Glue job properties.
B) Choose the STANDARD execution class in the Glue job properties.
C) Use the Spot Instance type in Glue job properties.
D) Choose the FLEX execution class in the Glue job properties.

5. A company uploads .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas.
An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately.
If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates.
Which solution will meet these requirements?

A) Use Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates. Write the data to the Redshift tables.
B) Use the AWS Glue ResolveChoice built-in transform to select the value of the column from the most recent record.
C) Modify the AWS Glue job to copy the rows into a staging Redshift table. Add SQL commands to update the existing rows with new values from the staging Redshift table.
D) Modify the AWS Glue job to load the previously inserted data into a MySQL database. Perform an upsert operation in the MySQL database. Copy the results to the Amazon Redshift tables.

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