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

NEW QUESTION # 42
A company stores customer data in an Amazon S3 bucket. The company must permanently delete all customer data that is older than 7 years.

Answer: A

Explanation:
S3 Lifecycle policies automate data retention and deletion. By specifying an expiration rule for 7 years, objects older than that period are permanently deleted without manual intervention.
"To automatically delete aged data, configure an S3 Lifecycle rule with an expiration policy for objects older than the retention period."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf


NEW QUESTION # 43
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.
References:
AWS Glue Job Bookmarks Documentation
AWS Glue Permissions for Bookmarks


NEW QUESTION # 44
A company stores server logs in an Amazon 53 bucket. The company needs to keep the logs for 1 year. The logs are not required after 1 year.
A data engineer needs a solution to automatically delete logs that are older than 1 year.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: A

Explanation:
Problem Analysis:
The company uses AWS Glue for ETL pipelines and requires automatic data quality checks during pipeline execution.
The solution must integrate with existing AWS Glue pipelines and evaluate data quality rules based on predefined thresholds.
Key Considerations:
Ensure minimal implementation effort by leveraging built-in AWS Glue features.
Use a standardized approach for defining and evaluating data quality rules.
Avoid custom libraries or external frameworks unless absolutely necessary.
Solution Analysis:
Option A: SQL Transform
Adding SQL transforms to define and evaluate data quality rules is possible but requires writing complex queries for each rule.
Increases operational overhead and deviates from Glue's declarative approach.
Option B: Evaluate Data Quality Transform with DQDL
AWS Glue provides a built-in Evaluate Data Quality transform.
Allows defining rules in Data Quality Definition Language (DQDL), a concise and declarative way to define quality checks.
Fully integrated with Glue Studio, making it the least effort solution.
Option C: Custom Transform with PyDeequ
PyDeequ is a powerful library for data quality checks but requires custom code and integration.
Increases implementation effort compared to Glue's native capabilities.
Option D: Custom Transform with Great Expectations
Great Expectations is another powerful library for data quality but adds complexity and external dependencies.
Final Recommendation:
Use Evaluate Data Quality transform in AWS Glue.
Define rules in DQDL for checking thresholds, null values, or other quality criteria.
This approach minimizes development effort and ensures seamless integration with AWS Glue.
AWS Glue Data Quality Overview
DQDL Syntax and Examples
Glue Studio Transformations


NEW QUESTION # 45
A data engineer needs to create a new empty table in Amazon Athena that has the same schema as an existing table named old-table.
Which SQL statement should the data engineer use to meet this requirement?

Answer: A

Explanation:
* Problem Analysis:
* The goal is to create a new empty table in Athena with the same schema as an existing table (old_table).
* The solution must avoid copying any data.
* Key Considerations:
* CREATE TABLE AS (CTAS) is commonly used in Athena for creating new tables based on an existing table.
* Adding the WITH NO DATA clause ensures only the schema is copied, without transferring any data.
* Solution Analysis:
* Option A: Copies both schema and data. Does not meet the requirement for an empty table.
* Option B: Inserts data into an existing table, which does not create a new table.
* Option C: Creates an empty table but does not copy the schema.
* Option D: Creates a new table with the same schema and ensures it is empty by using WITH NO DATA.
* Final Recommendation:
* Use D. CREATE TABLE new_table AS (SELECT * FROM old_table) WITH NO DATA to create an empty table with the same schema.
Athena CTAS Queries
CREATE TABLE Statement in Athena


NEW QUESTION # 46
A banking company uses an application to collect large volumes of transactional data. The company uses Amazon Kinesis Data Streams for real-time analytics. The company's application uses the PutRecord action to send data to Kinesis Data Streams.
A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.
Which solution will meet this requirement?

Answer: D

Explanation:
For exactly-once delivery and processing in Amazon Kinesis Data Streams, the best approach is to design the application so that it handlesidempotency. By embedding aunique IDin each record, the application can identify and remove duplicate records during processing.
* Exactly-Once Processing:
* Kinesis Data Streams does not natively support exactly-once processing. Therefore,idempotency should be designed into the application, ensuring that each record has a unique identifier so that the same event is processed only once, even if it is ingested multiple times.
* This pattern is widely used for achieving exactly-once semantics in distributed systems.
Reference:Building Idempotent Applications with Kinesis
Alternatives Considered:
B (Checkpoint configuration): While updating the checkpoint configuration can help with some aspects of duplicate processing, it is not a full solution for exactly-once delivery.
C (Design data source): Ensuring events are not ingested multiple times is ideal, but network outages can make this difficult, and it doesn't guarantee exactly-once delivery.
D (Using EMR): While using EMR with Flink or Spark could work, it introduces unnecessary complexity compared to handling idempotency at the application level.
References:
Amazon Kinesis Best Practices for Exactly-Once Processing
Achieving Idempotency with Amazon Kinesis


NEW QUESTION # 47
......

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