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[Hardware] 2026 Data-Engineer-Associate Detailed Answers | High Pass-Rate Data-Engineer-Ass

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【Hardware】 2026 Data-Engineer-Associate Detailed Answers | High Pass-Rate Data-Engineer-Ass

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q84-Q89):NEW QUESTION # 84
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.
The company receives 2 GB erf sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.
Which solution will meet these requirements with the LEAST operational overhead?
  • A. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with sales opportunities. Use AWS Step Functions to orchestrate the process.
  • B. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use Amazon Kinesis Data Streams to fetch sales records from the MySQL database. Use Amazon Managed Service for Apache Flink to correlate the datasets. Use AWS Step Functions to orchestrate the process.
  • C. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with the sales opportunities. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the process.
  • D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to fetch both datasets. Use AWS Lambda functions to correlate the datasets. Use AWS Step Functions to orchestrate the process.
Answer: A
Explanation:
Problem Analysis:
The company processes 2 GB of daily sales records and 100 GB of Salesforce sales opportunities.
The goal is to analyze and correlate the two datasets with low operational overhead.
The process must run once nightly.
Key Considerations:
Amazon AppFlow simplifies data integration with Salesforce.
AWS Glue can extract data from MySQL and perform ETL operations.
Step Functions can orchestrate workflows with minimal manual intervention.
Apache Airflow and Flink add complexity, which conflicts with the requirement for low operational overhead.
Solution Analysis:
Option A: MWAA + Lambda + Step Functions
Requires custom Lambda code for dataset correlation, increasing development and operational complexity.
Option B: AppFlow + Glue + MWAA
MWAA adds orchestration overhead compared to the simpler Step Functions.
Option C: AppFlow + Glue + Step Functions
AppFlow fetches Salesforce data, Glue extracts MySQL data, and Step Functions orchestrate the entire process.
Minimal setup and operational overhead, making it the best choice.
Option D: AppFlow + Kinesis + Flink + Step Functions
Using Kinesis and Flink for batch processing introduces unnecessary complexity.
Final Recommendation:
Use Amazon AppFlow to fetch Salesforce data, AWS Glue to process MySQL data, and Step Functions for orchestration.
Reference:
Amazon AppFlow Overview
AWS Glue ETL Documentation
AWS Step Functions

NEW QUESTION # 85
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.
The company receives 2 GB erf sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.
Which solution will meet these requirements with the LEAST operational overhead?
  • A. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with sales opportunities. Use AWS Step Functions to orchestrate the process.
  • B. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use Amazon Kinesis Data Streams to fetch sales records from the MySQL database. Use Amazon Managed Service for Apache Flink to correlate the datasets. Use AWS Step Functions to orchestrate the process.
  • C. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with the sales opportunities. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the process.
  • D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to fetch both datasets. Use AWS Lambda functions to correlate the datasets. Use AWS Step Functions to orchestrate the process.
Answer: A
Explanation:
Problem Analysis:
The company processes 2 GB of daily sales records and 100 GB of Salesforce sales opportunities.
The goal is to analyze and correlate the two datasets with low operational overhead.
The process must run once nightly.
Key Considerations:
Amazon AppFlow simplifies data integration with Salesforce.
AWS Glue can extract data from MySQL and perform ETL operations.
Step Functions can orchestrate workflows with minimal manual intervention.
Apache Airflow and Flink add complexity, which conflicts with the requirement for low operational overhead.
Solution Analysis:
Option A: MWAA + Lambda + Step Functions
Requires custom Lambda code for dataset correlation, increasing development and operational complexity.
Option B: AppFlow + Glue + MWAA
MWAA adds orchestration overhead compared to the simpler Step Functions.
Option C: AppFlow + Glue + Step Functions
AppFlow fetches Salesforce data, Glue extracts MySQL data, and Step Functions orchestrate the entire process.
Minimal setup and operational overhead, making it the best choice.
Option D: AppFlow + Kinesis + Flink + Step Functions
Using Kinesis and Flink for batch processing introduces unnecessary complexity.
Final Recommendation:
Use Amazon AppFlow to fetch Salesforce data, AWS Glue to process MySQL data, and Step Functions for orchestration.
Amazon AppFlow Overview
AWS Glue ETL Documentation
AWS Step Functions

NEW QUESTION # 86
A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.
The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team.
The compliance team must have access to a different subset of columns than the marketing team needs access to.
Which combination of steps should the data engineer take to meet these requirements? (Select TWO.)
  • A. Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.
  • B. Create views of the tables that need to be shared. Include only the required columns.
  • C. Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.
  • D. Create an Amazon Redshift data than that includes the tables that need to be shared.
  • E. Create an Amazon Redshift managed VPC endpoint in the marketing team's account. Grant the marketing team access to the views.
Answer: B,C
Explanation:
The company is using a data mesh architecture with AWS Lake Formation for governance and needs to share specific subsets of data with different teams (marketing and compliance) using Amazon Redshift Serverless.
* Option A: Create views of the tables that need to be shared. Include only the required columns.
Creating views in Amazon Redshift that include only the necessary columns allows for fine-grained access control. This method ensures that each team has access to only the data they are authorized to view.
* Option E: Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.Amazon Redshift data sharing enables live access to data across Redshift clusters or Serverless workgroups. By sharing data with specific workgroups, you can ensure that the marketing team and compliance team each access the relevant subset of data based on the views created.
* Option B (creating a Redshift data share) is close but does not address the fine-grained column-level access.
* Option C (creating a managed VPC endpoint) is unnecessary for sharing data with specific teams.
* Option D (sharing with the Lake Formation catalog) is incorrect because Redshift data shares do not integrate directly with Lake Formation catalogs; they are specific to Redshift workgroups.
References:
* Amazon Redshift Data Sharing
* AWS Lake Formation Documentation

NEW QUESTION # 87
A data engineer is implementing model governance for machine learning (ML) workflows on AWS. The data engineer needs a solution that can track the complete lifecycle of the ML models, including data preparation, model training, and deployment stages. The solution must ensure reproducibility and audit compliance.
  • A. Use Amazon SageMaker Experiments to create associations between datasets and artifacts by tracking hyperparameters and metrics.
  • B. Use Amazon SageMaker Debugger to capture metrics. Create associations between datasets and training jobs by monitoring training jobs.
  • C. Use Amazon SageMaker ML Lineage Tracking to create associations between artifacts, training jobs, and datasets by recording metadata.
  • D. Use Amazon SageMaker Model Monitor to create associations between artifacts and training jobs by tracking model performance.
Answer: C
Explanation:
Amazon SageMaker ML Lineage Tracking provides a unified mechanism to automatically capture and track the lineage of ML artifacts (datasets, jobs, and models). It supports auditability, reproducibility, and compliance by maintaining metadata for every stage of the ML lifecycle.
"Use Amazon SageMaker Lineage Tracking to automatically record and track the lineage of data, training jobs, models, and endpoints to ensure auditability and reproducibility."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

NEW QUESTION # 88
A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size.
A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file.
Which solution will meet this requirement with the LEAST operational effort?
  • A. Create and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.
  • B. Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.
  • C. Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.
  • D. Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.
Answer: D

NEW QUESTION # 89
......
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