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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q99-Q104):

NEW QUESTION # 99
A company has a data lake in Amazon 53. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.
The company needs to ensure that data quality issues are checked every time the pipelines run. A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.
Which solution will meet these requirements with the LEAST implementation effort?

A. Add a new Evaluate Data Quality transform to each Glue ETL job. Use Data Quality Definition Language (DQDL) to implement a ruleset that includes the data quality rules that need to be evaluated.
B. Add a new custom transform to each Glue ETL job. Use the PyDeequ library to implement a ruleset that includes the data quality rules that need to be evaluated.
C. Add a new custom transform to each Glue ETL job. Use the Great Expectations library to implement a ruleset that includes the data quality rules that need to be evaluated.
D. Add a new transform that is defined by a SQL query to each Glue ETL job. Use the SQL query to implement a ruleset that includes the data quality rules that need to be evaluated.

Answer: A

Explanation:
Problem Analysis:
The company uses AWS Glue for ETL pipelines and must enforce data quality checks during pipeline execution.
The goal is to implement quality checks with minimal implementation effort.
Key Considerations:
AWS Glue provides an Evaluate Data Quality transform that allows for defining quality checks directly in the pipeline.
DQDL (Data Quality Definition Language) simplifies the process by allowing declarative rule definitions.
Solution Analysis:
Option A: SQL Transform
SQL queries can implement rules but require manual effort for each rule and do not integrate natively with Glue.
Option B: Evaluate Data Quality Transform + DQDL
AWS Glue's built-in Evaluate Data Quality transform is designed for this use case.
Allows defining thresholds and rules in DQDL with minimal coding effort.
Option C: Custom Transform with PyDeequ
PyDeequ is a powerful library but adds unnecessary complexity compared to Glue's native features.
Option D: Custom Transform with Great Expectations
Similar to PyDeequ, Great Expectations adds operational complexity and external dependencies.
Final Recommendation:
Use the Evaluate Data Quality transform with DQDL to implement data quality rules in AWS Glue pipelines.
Reference:
AWS Glue Data Quality
DQDL Syntax and Examples
AWS Glue Studio Documentation

NEW QUESTION # 100
A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?

A. Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves.
B. Load the data into Amazon Redshift. Create a view for each country. Create separate 1AM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.
C. Create a separate table for each country's customer data. Provide access to each analyst based on the country that the analyst serves.
D. Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company's access policies.

Answer: D

Explanation:
AWS Lake Formation is a service that allows you to easily set up, secure, and manage data lakes. One of the features of Lake Formation is row-level security, which enables you to control access to specific rows or columns of data based on the identity or role of the user. This feature is useful for scenarios where you need to restrict access to sensitive or regulated data, such as customer data from different countries. By registering the S3 bucket as a data lake location in Lake Formation, you can use the Lake Formation console or APIs to define and apply row-level security policies to the data in the bucket. You can also use Lake Formation blueprints to automate the ingestion and transformation of data from various sources into the data lake. This solution requires the least operational effort compared to the other options, as it does not involve creating or moving data, or managing multiple tables, views, or roles. Reference:
AWS Lake Formation
Row-Level Security
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Lakes and Data Warehouses, Section 4.2: AWS Lake Formation

NEW QUESTION # 101
A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data.
Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.
Which solution will meet these requirements?

A. Create an AWS Glue job to redact the sensitive data and to load the data into a new Redshift table.
B. Enable metadata security on the Redshift cluster. Create IAM users and IAM roles for the customer support users and the audit users. Grant the IAM users and IAM roles permissions to view the metadata in the Redshift cluster.
C. Create a row-level security policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the security policy to the table.
D. Create a dynamic data masking policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the masking policy to the column that contains sensitive data.

Answer: D

Explanation:
Amazon Redshift supportsdynamic data masking, which enables you to limit sensitive data visibility to specific users and roles without duplicating the data. This approach supports showing only parts of a column's values (e.g., last four digits) and full visibility for authorized roles (e.g., auditors).
"With dynamic data masking, you can control how much sensitive data a user sees in query results without changing the data in the table."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf IAM roles are used to associate users with the appropriate masking rules, keeping security tight and avoiding the creation of duplicate data views or tables.

NEW QUESTION # 102
Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository's master branch as the source.
The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week's scheduled application release.
Which command should the developer for Branch B run before the developer raises a pull request to the master branch?

A. git fetch -b master
B. git rebase master
C. git pull master
D. git diff branchB master
git commit -m <message>

Answer: B

Explanation:
To ensure thatBranch Bis up to date with the latest changes in the master branch before submitting a pull request, the correct approach is to perform agit rebase. This command rewrites the commit history so that Branch B will be based on the latest changes in the master branch.
* git rebase master:
* This command moves the commits of Branch B to be based on top of the latest state of the master branch. It allows the developer to resolve any conflicts and create a clean history.
Reference:Git Rebase Documentation
Alternatives Considered:
A (git diff): This will only show differences between Branch B and master but won't resolve conflicts or bring Branch B up to date.
B (git pull master): Pulling the master branch directly does not offer the same clean history management as rebase.
D (git fetch -b): This is an incorrect command.
References:
Git Rebase Best Practices

NEW QUESTION # 103
An ecommerce company wants to use AWS to migrate data pipelines from an on-premises environment into the AWS Cloud. The company currently uses a third-party too in the on-premises environment to orchestrate data ingestion processes.
The company wants a migration solution that does not require the company to manage servers. The solution must be able to orchestrate Python and Bash scripts. The solution must not require the company to refactor any code.
Which solution will meet these requirements with the LEAST operational overhead?

A. AWS Glue
B. AWS Step Functions
C. AWS Lambda
D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

Answer: D

Explanation:
The ecommerce company wants to migrate its data pipelines into the AWS Cloud without managing servers, and the solution must orchestrate Python and Bash scripts without refactoring code. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is the most suitable solution for this scenario.
* Option B: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)MWAA is a managed orchestration service that supports Python and Bash scripts via Directed Acyclic Graphs (DAGs) for workflows. It is a serverless, managed version of Apache Airflow, which is commonly used for orchestrating complex data workflows, making it an ideal choice for migrating existing pipelines without refactoring. It supports Python, Bash, and other scripting languages, and the company would not need to manage the underlying infrastructure.
Other options:
* AWS Lambda (Option A) is more suited for event-driven workflows but would require breaking down the pipeline into individual Lambda functions, which may require refactoring.
* AWS Step Functions (Option C) is good for orchestration but lacks native support for Python and Bash without using Lambda functions, and it may require code changes.
* AWS Glue (Option D) is an ETL service primarily for data transformation and not suitable for orchestrating general scripts without modification.
References:
* Amazon Managed Workflows for Apache Airflow (MWAA) Documentation

NEW QUESTION # 104
......

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