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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q34-Q39):

NEW QUESTION # 34
A company stores logs in an Amazon S3 bucket. When a data engineer attempts to access several log files, the data engineer discovers that some files have been unintentionally deleted.
The data engineer needs a solution that will prevent unintentional file deletion in the future.
Which solution will meet this requirement with the LEAST operational overhead?

Answer: C

Explanation:
To prevent unintentional file deletions and meet the requirement with minimal operational overhead, enabling S3 Versioningis the best solution.
* S3 Versioning:
* S3 Versioning allows multiple versions of an object to be stored in the same S3 bucket. When a file is deleted or overwritten, S3 preserves the previous versions, which means you canrecover from accidental deletions or modifications.
* Enabling versioning requires minimal overhead, as it is abucket-level settingand does not require additional backup processes or data replication.
* Users can recover specific versions of files that were unintentionally deleted, meeting the needs of the data engineer to avoid accidental data loss.
Reference:Amazon S3 Versioning
Alternatives Considered:
A (Manual backups): Manually backing up the bucket requires higher operational effort and maintenance compared to enabling S3 Versioning, which is automated.
C (S3 Replication): Replication ensures data is copied to another bucket but does not provide protection against accidental deletion. It would increase operational costs without solving the core issue of accidental deletion.
D (S3 Glacier): Storing data in Glacier provides long-term archival storage but is not designed to prevent accidental deletion. Glacier is also more suitable for archival and infrequently accessed data, not for active logs.
References:
Amazon S3 Versioning Documentation
S3 Data Protection Best Practices


NEW QUESTION # 35
A company stores a 100 MB dataset in an Amazon S3 bucket as an Apache Parquet file. A data engineer needs to profile the data before performing data preparation steps on the data.
Which solution will meet this requirement in the MOST operationally efficient way?

Answer: B

Explanation:
AWS Glue DataBrew is purpose-built for data profiling and data preparation with minimal operational overhead. A DataBrew profile job automatically analyzes datasets stored in Amazon S3 and generates detailed statistics such as column completeness, distinct values, data types, min/max values, and potential data quality issues. This is exactly what is required before data preparation steps.
The dataset size (100 MB) and Parquet format are fully supported by DataBrew, and no infrastructure provisioning, cluster management, or SQL development is required. The profiling results are produced automatically and can be reviewed directly in the AWS Management Console, making this the most efficient and lowest-effort solution.
Amazon Managed Service for Apache Flink is designed for real-time streaming analytics and introduces unnecessary complexity. Amazon Redshift Spectrum requires schema definition, SQL development, and additional setup, which is excessive for simple profiling. Amazon QuickSight is a visualization and BI service, not a data profiling tool.
Therefore, creating a DataBrew profile job is the most operationally efficient and exam-aligned solution.


NEW QUESTION # 36
Creating an EventBridge rule that triggers a Lambda function on AWS Glue job failure events and then sends notifications via Amazon SNS is the most direct and operationally efficient method:
"Practice Quiz 10: A data engineer must monitor the data pipeline... Which solution will meet these requirements?

Answer:

Explanation:
Inspect the job run monitoring section of the AWS Glue console.
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Although this reference directly supports using AWS Glue's monitoring features via EventBridge, it implies that solutions like A-which directly use EventBridge failure events for automation-are more optimal and less complex than constructing custom logs and metrics.


NEW QUESTION # 37
A university is developing an educational application that analyzes student essays. The application provides personalized feedback with accurate citations to the university ' s textbooks. The application needs to process essays in multiple languages. Application responses must include direct references to specific sections in the course materials and must be in the student ' s selected language.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
Option B is correct because Amazon Bedrock Knowledge Bases is the managed AWS service for retrieval- augmented generation using an organization's own content. AWS states that the RetrieveAndGenerate flow queries a knowledge base, generates responses based on retrieved results, and that the response includes citations only for relevant sources. AWS user guide pages also state that Knowledge Bases can return natural- language responses based on retrieved chunks from source documents. This directly fits the requirement for feedback with direct references to textbook sections.
This is also the least operational overhead choice because Amazon Bedrock manages ingestion, chunking, embedding, retrieval, and generation workflow components. Option A would require building and operating a custom vector solution. Option C adds multiple services and custom glue logic. Option D requires custom model hosting and fine-tuning, which is far more operationally heavy. For multilingual processing, Amazon Bedrock supports multilingual-capable models and multilingual embedding options, making it suitable for responses in the student's selected language when paired with an appropriate model.


NEW QUESTION # 38
A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB. The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)

Answer: D,E

Explanation:
Using an Amazon EventBridge rule to run an AWS Glue job or invoke an AWS Glue workflow job every 15 minutes are two possible solutions that will meet the requirements. AWS Glue is a serverless ETL service that can process and load data from various sources to various targets, including Amazon Redshift. AWS Glue can handle different data formats, such as CSV, JSON, and Parquet, and also support schema evolution, meaning it can adapt to changes in the data schema over time. AWS Glue can also leverage Apache Spark to perform distributed processing and transformation of large datasets. AWS Glue integrates with Amazon EventBridge, which is a serverless event bus service that can trigger actions based on rules and schedules. By using an Amazon EventBridge rule, you can invoke an AWS Glue job or workflow every 15 minutes, and configure the job or workflow to run an AWS Glue crawler and then load the data into the Amazon Redshift tables. This way, you can build a cost-effective and scalable ETL pipeline that can handle data from 10 source systems and function correctly despite changes to the data schema.
The other options are not solutions that will meet the requirements. Option C, configuring an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket, and creating a second Lambda function to run the AWS Glue job, is not a feasible solution, as it would require a lot of Lambda invocations and coordination. AWS Lambda has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the ETL pipeline. Option D, configuring an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket, is not a necessary solution, as you can use an Amazon EventBridge rule to invoke the AWS Glue workflow directly, without the need for a Lambda function. Option E, configuring an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket, and configuring the AWS Glue job to put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream, is not a cost-effective solution, as it would incur additional costs for Lambda invocations and data delivery. Moreover, using Amazon Kinesis Data Firehose to load data into Amazon Redshift is not suitable for frequent and small batches of data, as it can cause performance issues and data fragmentation. Reference:
AWS Glue
Amazon EventBridge
Using AWS Glue to run ETL jobs against non-native JDBC data sources
[AWS Lambda quotas]
[Amazon Kinesis Data Firehose quotas]


NEW QUESTION # 39
......

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