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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q42-Q47):

NEW QUESTION # 42
A company uses Amazon S3 and AWS Glue Data Catalog to manage a data lake that contains contact information for customers. The company uses PySpark and AWS Glue jobs with a DynamicFrame to run a workflow that processes data within the data lake.
A data engineer notices that the workflow is generating errors as a result of how customer postal codes are stored in the data lake. Some postal codes include unnecessary numbers or invalid characters.
The data engineer needs a solution to address the errors and correct the postal codes in the data lake.
Which solution will meet these requirements?

Answer: A

Explanation:
Option A is the only choice that directly addresses the root cause: inconsistent postal-code formatting causing processing errors. In AWS Glue, a DynamicFrame can encounter issues when incoming data contains unexpected types or malformed values. Providing an explicit PySpark schema forces the postal code column to be interpreted consistently (for example, as a string with the expected structure), which prevents downstream steps from failing because of unexpected characters or mixed representations. After enforcing a consistent schema, the Glue job can standardize values (such as trimming extra digits, removing invalid characters, and normalizing case) and then write the corrected output back to Amazon S3 so the data lake is fixed at the source.
The document reinforces that AWS Glue is the managed ETL service used to transform data and move it between stores, which is exactly what is needed to correct bad values and persist clean outputs back into the lake. It also highlights that AWS Glue can perform validation as part of the ETL process, supporting the idea that data can be checked and corrected during processing rather than allowing bad values to break the workflow.
Options B, C, and D do not fix data quality or formatting issues; they relate to workflow state sharing, partition filtering, or internal implementation details, not cleansing invalid postal codes.


NEW QUESTION # 43
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time.
Which solution will meet these requirements?

Answer: C

Explanation:
Comprehensive and Detailed Explanation (150-250 words)
Amazon Athena Federated Query enables SQL queries across multiple data sources without moving data.
Athena can directly query Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3 using data source connectors. This capability is ideal for one-time or ad hoc analytical queries, where setting up ingestion pipelines would introduce unnecessary cost and delay.
Because the requirement is for a one-time performance report, the solution should minimize operational overhead and data duplication. Athena Federated Query executes queries in place, eliminating ETL processes, data exports, and staging layers. This significantly reduces query preparation time and avoids storage costs associated with copying data into a centralized location.
The other options require extensive data movement and setup. DynamoDB Streams, AWS DMS, Glue ETL pipelines, and Amazon EMR clusters are appropriate for ongoing analytics pipelines, not for a single analytical report. These approaches increase operational complexity and execution time.
Athena Federated Query is purpose-built for this exact scenario and provides the fastest path to insight with minimal overhead.


NEW QUESTION # 44
A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The data engineer enabled the bookmark feature for the AWS Glue job. The data engineer has set the maximum concurrency for the AWS Glue job to 1.
The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.
What is the likely reason the AWS Glue job is reprocessing the files?

Answer: C

Explanation:
The issue described is that the AWS Glue job is reprocessing files from previous runs despite the bookmark feature being enabled. Bookmarks in AWS Glue allow jobs to keep track of which files or data have already been processed to avoid reprocessing. The most likely reason for reprocessing the files is missing S3 permissions, specifically s3
.
s3
is a permission required by AWS Glue when bookmarks are enabled to ensure Glue can retrieve metadata from the files in S3, which is necessary for the bookmark mechanism to function correctly. Without this permission, Glue cannot track which files have been processed, resulting in reprocessing during subsequent runs.
Concurrency settings (Option B) and the version of AWS Glue (Option C) do not affect the bookmark behavior. Similarly, the lack of a commit statement (Option D) is not applicable in this context, as Glue handles commits internally when interacting with Redshift and S3.
Thus, the root cause is likely related to insufficient permissions on the S3 bucket, specifically s3
, which is required for bookmarks to work as expected.
Reference:
AWS Glue Job Bookmarks Documentation
AWS Glue Permissions for Bookmarks


NEW QUESTION # 45
A healthcare company stores patient records in an on-premises MySQL database. The company creates an application to access the MySQL database. The company must enforce security protocols to protect the patient records. The company currently rotates database credentials every 30 days to minimize the risk of unauthorized access.
The company wants a solution that does not require the company to modify the application code for each credential rotation.
Which solution will meet this requirement with the least operational overhead?

Answer: D

Explanation:
The correct solution is C: AWS Secrets Manager.
* Why? AWS Secrets Manager is a fully managed service that helps you protect access to your applications, services, and IT resources without the upfront cost and complexity of managing your own hardware security module (HSM) infrastructure.
* It allows for automatic rotation of secrets without requiring changes to the application code, meeting the requirement of minimal operational overhead.
* Applications can securely retrieve credentials using Secrets Manager APIs, and the service integrates with AWS Identity and Access Management (IAM) to control access to secrets.
"You can use Secrets Manager to store credentials and to configure automatic rotation." Reference: AWS Certified Data Engineer - Associate Study Guide, Chapter 7 - Data Security and GovernanceAlso verified in AWS Documentation: https://docs.aws.amazon.com/secretsmanager/latest
/userguide/rotating-secrets.html


NEW QUESTION # 46
A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3.
Which actions will provide the FASTEST queries? (Choose two.)

Answer: A,B


NEW QUESTION # 47
......

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