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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Data Security and Governance	18%	- Implement access control and authentication 1. IAM, Lake Formation, resource policies - Protect sensitive data - Enforce compliance and data governance 1. Data lineage, audit logging, regulatory requirements - Encrypt data at rest and in transit
Data Store Management	26%	- Manage data lifecycle and storage tiers - Design and implement data storage solutions 1. Data lakes, data warehouses, databases 2. S3, Redshift, DynamoDB, RDS, Lake Formation - Optimize storage performance and cost
Data Ingestion and Transformation	34%	- Ingest data from various sources 1. Use services like Kinesis, DMS, Glue, S3 2. Batch and streaming data ingestion - Implement data quality and validation - Transform and enrich data 1. Apply data processing logic 2. Use Spark, EMR, Step Functions 3. Orchestrate data pipelines
Data Operations and Support	22%	- Monitor and troubleshoot data pipelines 1. CloudWatch, X-Ray, logging and metrics - Ensure reliability and scalability - Automate operational tasks - Backup, restore, and disaster recovery

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q132-Q137):

NEW QUESTION # 132
A retail company stores data from a product lifecycle management (PLM) application in an on-premises MySQL database. The PLM application frequently updates the database when transactions occur.
The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.
The company has already established an AWS Direct Connect connection between the on-premises infrastructure and AWS.
Which solution will meet these requirements with the LEAST development effort?

A. Run a scheduled AWS Glue extract, transform, and load (ETL) job to get the MySQL database updates by using a Java Database Connectivity (JDBC) connection. Set Amazon Redshift as the destination for the ETL job.
B. Run a full load plus CDC task in AWS Database Migration Service (AWS DMS) to continuously replicate the MySQL database changes. Set Amazon Redshift as the destination for the task.
C. Use the Amazon AppFlow SDK to build a custom connector for the MySQL database to continuously replicate the database changes. Set Amazon Redshift as the destination for the connector.
D. Run scheduled AWS DataSync tasks to synchronize data from the MySQL database. Set Amazon Redshift as the destination for the tasks.

Answer: B

Explanation:
* Problem Analysis:
* The company needs near real-time replication of MySQL updates to Amazon Redshift.
* Minimal development effort is required for this solution.
* Key Considerations:
* AWSDMSprovides afull load + CDC (Change Data Capture)mode for continuous replication of database changes.
* DMS integrates natively with both MySQL and Redshift, simplifying setup.
* Solution Analysis:
* Option A: AWS Glue Job
* Glue is batch-oriented and does not support near real-time replication.
* Option B: DMS with Full Load + CDC
* Efficiently handles initial database load and continuous updates.
* Requires minimal setup and operational overhead.
* Option C: AppFlow SDK
* AppFlow is not designed for database replication. Custom connectors increase development effort.
* Option D: DataSync
* DataSync is for file synchronization and not suitable for database updates.
* Final Recommendation:
* UseAWS DMSinfull load + CDCmode for continuous replication.
:
AWS Database Migration Service Documentation
Setting Up DMS with Redshift

NEW QUESTION # 133
A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket.
The process needs to rename a column, remove specific columns, ignore the second row of each file, create a new column based on the values of the first row of the data, and filter the results by a numeric value of a column.
Which solution will meet these requirements with the LEAST development effort?

A. Use an AWS Glue custom crawler to read and transform the CSV files.
B. Use AWS Glue Python jobs to read and transform the CSV files.
C. Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.
D. Use AWS Glue DataBrew recipes to read and transform the CSV files.

Answer: D

Explanation:
The requirement involves transforming CSV files by renaming columns, removing rows, and other operations with minimal development effort. AWS Glue DataBrew is the best solution here because it allows you to visually create transformation recipes without writing extensive code.
* Option D: Use AWS Glue DataBrew recipes to read and transform the CSV files.DataBrew provides a visual interface where you can build transformation steps (e.g., renaming columns, filtering rows, creating new columns, etc.) as a "recipe" that can be applied to datasets, making it easy to handle complex transformations on CSV files with minimal coding.
Other options (A, B, C) involve more manual development and configuration effort (e.g., writing Python jobs or creating custom workflows in Glue) compared to the low-code/no-code approach of DataBrew.
References:
* AWS Glue DataBrew Documentation

NEW QUESTION # 134
A company stores its processed data in an S3 bucket. The company has a strict data access policy. The company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.
The company wants to receive notifications when a user violates the data access policy. Each notification must include the username of the user who violated the policy.
Which solution will meet these requirements?

A. Use AWS Config rules to detect violations of the data access policy. Set up compliance alarms.
B. Use AWS CloudTrail to track object-level events for the S3 bucket. Forward events to Amazon CloudWatch to set up CloudWatch alarms.
C. Use Amazon CloudWatch metrics to gather object-level metrics. Set up CloudWatch alarms.
D. Use Amazon S3 server access logs to monitor access to the bucket. Forward the access logs to an Amazon CloudWatch log group. Use metric filters on the log group to set up CloudWatch alarms.

Answer: B

Explanation:
The requirement is to detect violations of data access policies and receive notifications with the username of the violator. AWS CloudTrail can provideobject-level tracking for S3to capture detailed API actions on specific S3 objects, including the user who performed the action.
* AWS CloudTrail:
* CloudTrail can monitorAPI callsmade to an S3 bucket, including object-level API actions such as GetObject, PutObject, and DeleteObject. This will help detect access violations based on the API calls made by different users.
* CloudTrail logs include details such as theuser identity, which is essential for meeting the requirement of including the username in notifications.
* The CloudTrail logs can be forwarded toAmazon CloudWatchto trigger alarms based on certain access patterns (e.g., violations of specific policies).
Reference:Monitoring Amazon S3 Activity Using AWS CloudTrail
Amazon CloudWatch:
By forwarding CloudTrail logs to CloudWatch, you can set upalarmsthat are triggered when a specific condition is met, such as unauthorized access or policy violations. The alarm can include detailed information from the CloudTrail log, including the username.
Alternatives Considered:
A (AWS Config rules): While AWS Config can track resource configurations and compliance, it does not provide real-time, detailed tracking of object-level events like CloudTrail does.
B (CloudWatch metrics): CloudWatch does not gather object-level metrics for S3 directly. For this use case, CloudTrail provides better granularity.
D (S3 server access logs): S3 server access logs can monitor access, but they do not provide the real-time monitoring and alerting features that CloudTrail with CloudWatch alarms offer. They also do not include API- level granularity like CloudTrail.
References:
AWS CloudTrail Integration with S3
Amazon CloudWatch Alarms

NEW QUESTION # 135
A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.
Which solution will meet these requirements with the LEAST development effort?

A. Use Kinesis Data Firehose to convert the csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.
B. Use Kinesis Data Firehose to convert the csv files to JSON and to store the files in Parquet format.
C. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.
D. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON.
Use Kinesis Data Firehose to store the files in Parquet format.

Answer: B

Explanation:
The company wants to use Amazon Kinesis Data Firehose to transform CSV files into JSON format and store the files in Apache Parquet format with the least development effort.
Option B: Use Kinesis Data Firehose to convert the CSV files to JSON and to store the files in Parquet format.
Kinesis Data Firehose supports data format conversion natively, including converting incoming CSV data to JSON format and storing the resulting files in Parquet format in Amazon S3. This solution requires the least development effort because it uses built-in transformation features of Kinesis Data Firehose.
Other options (A, C, D) involve invoking AWS Lambda functions, which would introduce additional complexity and development effort compared to Kinesis Data Firehose's native format conversion capabilities.
References:
Amazon Kinesis Data Firehose Documentation

NEW QUESTION # 136
A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII).
The company has an internal analytics application that does not require access to the PII.
To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset.
Which solution will meet the requirements with the LEAST operational overhead?

A. Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.
B. Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset.
Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.
C. Use AWS Glue to transform the data for each application. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.
D. Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.

Answer: A

Explanation:
Option B is the best solution to meet the requirements with the least operational overhead because S3 Object Lambda is a feature that allows you to add your own code to process data retrieved from S3 before returning it to an application. S3 Object Lambda works with S3 GET requests and can modify both the object metadata and the object data. By using S3 Object Lambda, you can implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data. This way, you can avoid creating and maintaining multiple copies of the dataset with different levels of redaction.
Option A is not a good solution because it involves creating and managing multiple copies of the dataset with different levels of redaction for each application. This option adds complexity and storage cost to the data protection process and requires additional resources and configuration. Moreover, S3 bucket policies cannot enforce fine-grained data access control at the row and column level, so they are not sufficient to redact PII.
Option C is not a good solution because it involves using AWS Glue to transform the data for each application. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. However, in this scenario, using AWS Glue to redact PII is not the best option because it requires creating and maintaining multiple copies of the dataset with different levels of redaction for each application. This option also adds extra time and cost to the data protection process and requires additional resources and configuration.
Option D is not a good solution because it involves creating and configuring an API Gateway endpoint that has custom authorizers. API Gateway is a service that allows youto create, publish, maintain, monitor, and secure APIs at any scale. API Gateway can also integrate with other AWS services, such as Lambda, to provide custom logic for processing requests. However, in this scenario, using API Gateway to redact PII is not the best option because it requires writing and maintaining custom code and configuration for the API endpoint, the custom authorizers, and the REST API call. This option also adds complexity and latency to the data protection process and requires additional resources and configuration.
References:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
Introducing Amazon S3 Object Lambda - Use Your Code to Process Data as It Is Being Retrieved from S3 Using Bucket Policies and User Policies - Amazon Simple Storage Service AWS Glue Documentation What is Amazon API Gateway? - Amazon API Gateway

NEW QUESTION # 137
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