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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Security and Governance	18%	- Implement data security controls - Apply governance and compliance best practices
Topic 2: Data Operations and Support	22%	- Monitor and maintain data pipelines - Troubleshoot data workflow issues
Topic 3: Data Store Management	26%	- Select appropriate data storage solutions - Optimize storage performance and cost
Topic 4: Data Ingestion and Transformation	34%	- Build and manage data pipelines - Ingest and transform data using AWS services

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q153-Q158):

NEW QUESTION # 153
A data engineer is building a data pipeline. A large data file is uploaded to an Amazon S3 bucket once each day at unpredictable times. An AWS Glue workflow uses hundreds of workers to process the file and load the data into Amazon Redshift. The company wants to process the file as quickly as possible.
Which solution will meet these requirements?

A. Create a scheduled AWS Glue trigger to start the workflow. Create a cron job that runs the AWS Glue job every 15 minutes. Set up the AWS Glue job to check the S3 bucket for the daily file. Configure the job to stop if the file is not present.
B. Create an event-based AWS Glue trigger to start the workflow. Configure Amazon S3 to log events to AWS CloudTrail. Create a rule in Amazon EventBridge to forward PutObject events to the AWS Glue trigger.
C. Create an on-demand AWS Glue trigger to start the workflow. Create an AWS Database Migration Service (AWS DMS) migration task. Set the DMS source as the S3 bucket. Set the target endpoint as the AWS Glue workflow.
D. Create an on-demand AWS Glue trigger to start the workflow. Create an AWS Lambda function that runs every 15 minutes to check the S3 bucket for the daily file. Configure the function to start the AWS Glue workflow if the file is present.

Answer: B

Explanation:
The best solution for fast, event-driven processing of unpredictable file uploads is to use S3 event notifications, CloudTrail, and EventBridge to automatically trigger the AWS Glue workflow:
"You can configure S3 PutObject events to be captured by CloudTrail and forwarded through EventBridge to trigger an AWS Glue job or workflow. This allows Glue to begin processing as soon as the file arrives, with minimal latency."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This option provides the lowest latency and least manual overhead compared to polling or scheduling solutions.

NEW QUESTION # 154
A data engineer is building a solution to detect sensitive information that is stored in a data lake across multiple Amazon S3 buckets. The solution must detect personally identifiable information (PII) that is in a proprietary data format.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Macie with managed data identifiers.
B. Use Amazon Athena with a SQL query to match the custom formats.
C. Use an AWS Lambda function with custom regular expressions.
D. Use the AWS Glue Detect PII transform with specific patterns.

Answer: D

Explanation:
Comprehensive and Detailed Explanation (150-250 words)
AWS Glue Detect PII transform is designed to identify sensitive data using custom pattern matching, making it well suited for detecting PII in proprietary or non-standard data formats. The transform integrates directly into AWS Glue ETL jobs and requires minimal configuration beyond defining the detection patterns.
Amazon Macie primarily relies on managed data identifiers and machine learning models optimized for common data formats such as JSON, CSV, and text. While Macie supports custom identifiers, it is less efficient for deeply proprietary formats and introduces additional service configuration.
Using AWS Lambda with custom regular expressions or Amazon Athena with SQL-based pattern matching would require building, operating, and maintaining custom logic across multiple buckets, increasing operational overhead and complexity.
AWS Glue provides a serverless, scalable, and centralized approach for PII detection as part of an existing data processing pipeline, making it the most operationally efficient solution for this requirement.
Therefore, Option A is the best answer.

NEW QUESTION # 155
A data engineer needs to onboard a new data producer into AWS. The data producer needs to migrate data products to AWS.
The data producer maintains many data pipelines that support a business application. Each pipeline must have service accounts and their corresponding credentials. The data engineer must establish a secure connection from the data producer's on-premises data center to AWS. The data engineer must not use the public internet to transfer data from an on-premises data center to AWS.
Which solution will meet these requirements?

A. Create an AWS Direct Connect connection to the on-premises data center. Store the service account credentials in AWS Secrets manager.
B. Create an AWS Direct Connect connection to the on-premises data center. Store the application keys in AWS Secrets Manager. Create Amazon S3 buckets that contain resigned URLS that have one-day expiration dates.
C. Create a security group in a public subnet. Configure the security group to allow only connections from the CIDR blocks that correspond to the data producer. Create Amazon S3 buckets than contain presigned URLS that have one-day expiration dates.
D. Instruct the new data producer to create Amazon Machine Images (AMIs) on Amazon Elastic Container Service (Amazon ECS) to store the code base of the application. Create security groups in a public subnet that allow connections only to the on-premises data center.

Answer: A

Explanation:
For secure migration of data from an on-premises data center to AWS without using the public internet, AWS Direct Connect is the most secure and reliable method. Using Secrets Manager to store service account credentials ensures that the credentials are managed securely with automatic rotation.
* AWS Direct Connect:
* Direct Connect establishes a dedicated, private connection between the on-premises data center and AWS, avoiding the public internet. This is ideal for secure, high-speed data transfers.

NEW QUESTION # 156
A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?

A. Option C
B. Option B
C. Option D
D. Option A

Answer: D

Explanation:
To create a new table named cities_usa in Amazon Athena based on a subset of data from the existing cities_world table, you should use an INSERT INTO statement combined with a SELECT statement to filter only the records where the country is 'usa'. The correct SQL syntax would be:
Option A: INSERT INTO cities_usa (city, state) SELECT city, state FROM cities_world WHERE country='usa'; This statement inserts only the cities and states where the country column has a value of 'usa' from the cities_world table into the cities_usa table. This is a correct approach to create a new table with data filtered from an existing table in Athena.
Options B, C, and D are incorrect due to syntax errors or incorrect SQL usage (e.g., the MOVE command or the use of UPDATE in a non-relevant context).
Reference:
Amazon Athena SQL Reference
Creating Tables in Athena

NEW QUESTION # 157
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. Create a separate table for each country's customer data. Provide access to each analyst based on the country that the analyst serves.
B. 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.
C. 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.
D. 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.

Answer: B

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. References:
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 # 158
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