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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) 認定 Data-Engineer-Associate 試験問題 (Q179-Q184):

質問 # 179
A company processes 500 GB of audience and advertising data daily, storing CSV files in Amazon S3 with schemas registered in AWS Glue Data Catalog. They need to convert these files to Apache Parquet format and store them in an S3 bucket.
The solution requires a long-running workflow with 15 GiB memory capacity to process the data concurrently, followed by a correlation process that begins only after the first two processes complete.

A. Use AWS Step Functions to orchestrate a workflow that uses multiple AWS Lambda functions. Ensure that the third process starts after the first two processes have finished.
B. Use Amazon EMR to run each process in the workflow. Create an Amazon Simple Queue Service (Amazon SQS) queue to handle messages that indicate the completion of the first two processes.
Configure an AWS Lambda function to process the SQS queue by running the third process.
C. Use AWS Glue workflows to run the first two processes in parallel. Ensure that the third process starts after the first two processes have finished.
D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the workflow by using AWS Glue. Configure AWS Glue to begin the third process after the first two processes have finished.

正解：C

解説：
AWS Glue Workflows can coordinate multiple ETL jobs and triggers. They support parallel execution and sequential dependencies, which is ideal for concurrent data processing followed by correlation steps, all with minimal operational overhead.
"Use AWS Glue Workflows to orchestrate multiple ETL jobs in sequence or in parallel, supporting conditional triggers and dependency management."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

質問 # 180
A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.
A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.
Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)

A. Enable fine-grained access control in AWS Lake Formation. Add a data filter for each Region.
B. Use data filters for each Region to register the S3 paths as data locations.
C. Modify the IAM roles of the HR departments to add a data filter for each department's Region.
D. Register the S3 path as an AWS Lake Formation location.
E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region.

正解：A、D

解説：
AWS Lake Formation is a service that helps you build, secure, and manage data lakes on Amazon S3. You can use AWS Lake Formation to register the S3 path as a data lake location, and enable fine-grained access control to limit access to the records based on the HR department's Region. You can use data filters to specify which S3 prefixes or partitions each HR department can access, and grant permissions to the IAM roles of the HR departments accordingly. This solution will meet the requirement with the least operational overhead, as it simplifies the data lake management and security, and leverages the existing IAM roles of the HR departments12.
The other options are not optimal for the following reasons:
A . Use data filters for each Region to register the S3 paths as data locations. This option is not possible, as data filters are not used to register S3 paths as data locations, but to grant permissions to access specific S3 prefixes or partitions within a data location. Moreover, this option does not specify how to limit access to the records based on the HR department's Region.
C . Modify the IAM roles of the HR departments to add a data filter for each department's Region. This option is not possible, as data filters are not added to IAM roles, but to permissions granted by AWS Lake Formation. Moreover, this option does not specify how to register the S3 path as a data lake location, or how to enable fine-grained access control in AWS Lake Formation.
E . Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region. This option is not recommended, as it would require more operational overhead to create and manage multiple S3 buckets, and to configure and maintain IAM policies for each HR department. Moreover, this option does not leverage the benefits of AWS Lake Formation, such as data cataloging, data transformation, and data governance.
Reference:
1: AWS Lake Formation
2: AWS Lake Formation Permissions
: AWS Identity and Access Management
: Amazon S3

質問 # 181
A company needs to transform IoT sensor data in near real time before the company stores the data in an Amazon S3 bucket. The data is available from a data stream in Amazon Kinesis Data Streams. The company needs to apply complex and stateful transformations to the data before the company stores the data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Configure an application in Amazon Managed Service for Apache Flink to process the data stream.
B. Schedule Apache Spark jobs on an Amazon EMR cluster to process the data stream.
C. Schedule AWS Glue ETL jobs to process the data stream.
D. Configure an AWS Lambda function to process the data stream.

正解：A

解説：
Option B is correct because Amazon Managed Service for Apache Flink is the AWS managed service built for real-time stream processing, including stateful computations over streaming data. AWS documentation describes Apache Flink as a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. AWS also states that Managed Service for Apache Flink can read from a Kinesis data stream, apply transformations such as filtering, aggregation, or enrichment, and write the transformed output to a sink. This maps exactly to the requirement for complex, stateful, near-real-time transformations on data coming from Amazon Kinesis Data Streams before storing it in S3.
Option A and D are less suitable because scheduled Glue or EMR Spark jobs are not the least-operational or best-fit solution for continuous near-real-time stateful streaming transformations. Option C can work for lightweight event processing, but Lambda is not the best fit for more complex and stateful streaming logic compared with Flink's native state management and streaming semantics. Therefore, Managed Service for Apache Flink is the correct managed AWS answer.

質問 # 182
A company saves customer data to an Amazon S3 bucket. The company uses server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes personally identifiable information (PII) such as social security numbers and account details.
Data that is tagged as PII must be masked before the company uses customer data for analysis. Some users must have secure access to the PII data during the preprocessing phase. The company needs a low- maintenance solution to mask and secure the PII data throughout the entire engineering pipeline.
Which combination of solutions will meet these requirements? (Select TWO.)

A. Configure an Amazon Made discovery job for the S3 bucket.
B. Write custom scripts in an application to mask the PII data and to control access.
C. Use Amazon GuardDuty to monitor access patterns for the PII data that is used in the engineering pipeline.
D. Use AWS Identity and Access Management (IAM) to manage permissions and to control access to the PII data.
E. Use AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask the PII data before analysis.

正解：D、E

解説：
To address the requirement of masking PII data and ensuring secure access throughout the data pipeline, the combination of AWS Glue DataBrew and IAM provides a low-maintenance solution.
* A. AWS Glue DataBrew for Masking:
* AWS Glue DataBrew provides a visual tool to perform data transformations, including masking PII data. It allows for easy configuration of data transformation tasks without requiring manual coding, making it ideal for this use case.

質問 # 183
A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AW5 Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Dairy.csv in a second 53 bucket.
Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day's CSV file.
A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.
Which solution will meet these requirements with the LEAST effort?

A. Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required files and empty files.
B. Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field.
C. Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.
D. Run a SQL query in Amazon Athena to read the CSV file and drop missing rows. Copy the corrected CSV file to the second S3 bucket.

正解：A

解説：
Problem Analysis:
The company runs a daily AWS Glue ETL pipeline to clean and transform files received in an S3 bucket.
If a file is incomplete or empty, the previous day's file should be retained.
Need a solution to validate files before overwriting the existing file.
Key Considerations:
Automate data validation with minimal human intervention.
Use built-in AWS Glue capabilities for ease of integration.
Ensure robust validation for missing or incomplete data.
Solution Analysis:
Option A: Lambda Function for Validation
Lambda can validate files, but it would require custom code.
Does not leverage AWS Glue's built-in features, adding operational complexity.
Option B: AWS Glue Data Quality Rules
AWS Glue Data Quality allows defining Data Quality Definition Language (DQDL) rules.
Rules can validate if required fields are missing or if the file is empty.
Automatically integrates into the existing ETL pipeline.
If validation fails, retain the previous day's file.
Option C: AWS Glue Studio with Filling Missing Values
Modifying ETL code to fill missing values with most common values risks introducing inaccuracies.
Does not handle empty files effectively.
Option D: Athena Query for Validation
Athena can drop rows with missing values, but this is a post-hoc solution.
Requires manual intervention to copy the corrected file to S3, increasing complexity.
Final Recommendation:
Use AWS Glue Data Quality to define validation rules in DQDL for identifying missing or incomplete data.
This solution integrates seamlessly with the ETL pipeline and minimizes manual effort.
Implementation Steps:
Enable AWS Glue Data Quality in the existing ETL pipeline.
Define DQDL Rules, such as:
Check if a file is empty.
Verify required fields are present and non-null.
Configure the pipeline to proceed with overwriting only if the file passes validation.
In case of failure, retain the previous day's file.
AWS Glue Data Quality Overview
Defining DQDL Rules
AWS Glue Studio Documentation

質問 # 184
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