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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Data Security and Governance	18%	- Implement data quality checks 1. AWS Glue DataBrew 2. Data validation - Apply authentication and authorization 1. Amazon S3 bucket policies 2. Service control policies (SCPs) 3. AWS IAM policies and roles - Manage data privacy and compliance 1. PII data handling 2. Data masking and tokenization 3. AWS Lake Formation permissions - Ensure data encryption 1. Encryption at rest and in transit 2. AWS KMS
Data Ingestion and Transformation	34%	- Transform and process data 1. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda) 2. Data partitioning and compression 3. ETL/ELT patterns 4. Batch and stream processing - Apply programming concepts 1. SQL, Python, Scala 2. Infrastructure as Code (IaC) 3. Version control - Orchestrate data pipelines 1. Event-driven architectures 2. AWS Glue Workflows 3. AWS Step Functions 4. Amazon Managed Workflows for Apache Airflow (MWAA) - Perform data ingestion 1. Batch data ingestion (scheduled ingestion, event-driven ingestion) 2. Throughput and latency characteristics for AWS services 3. Data ingestion patterns (frequency and data history) 4. Replayability of data 5. Streaming data ingestion
Data Operations and Support	22%	- Manage and troubleshoot data processes 1. Performance tuning 2. Cost optimization 3. Debugging failed jobs - Automate data pipelines 1. Scheduling jobs 2. Event-driven triggers 3. AWS Lambda triggers - Monitor data pipelines 1. AWS CloudTrail 2. Amazon CloudWatch 3. Logging and metrics
Data Store Management	26%	- Manage data lifecycle 1. Data archiving 2. Data retention policies 3. Amazon S3 storage classes - Understand data cataloging 1. Schema evolution 2. AWS Glue Data Catalog 3. Data discovery and classification - Design data models 1. Partitioning and indexing strategies 2. Schema design 3. Normalization and denormalization - Choose a data store 1. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift 2. Data lakes vs. data warehouses 3. Data characteristics (structured, semi-structured, unstructured) 4. Access and storage patterns

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q225-Q230):

NEW QUESTION # 225
A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand.
Which solution will meet these requirements?

A. Use Amazon ElastiCache (Redis OSS) and Amazon S3 to store product data. Use Amazon Athena to run flexible secondary attribute queries.
B. Use an Amazon DynamoDB table with on-demand capacity to store product data. Store products by primary key. Use global secondary indexes (GSIs) to store secondary attributes.
C. Use an Amazon OpenSearch Serverless cluster with dynamic scaling to store product data. Index product data by primary and secondary attributes.
D. Use Amazon Aurora with a Multi-AZ deployment to store product data. Use read replicas. Create indexes for primary and secondary attributes.

Answer: B

Explanation:
Option A is the correct design for single-digit millisecond latency with unpredictable spikes and variable attributes. The study material describes Amazon DynamoDB as a NoSQL database "designed for highly dynamic datasets with frequent read and write operations," providing low-latency performance at any scale- which directly matches the latency and traffic-spike requirements.
DynamoDB's key-value and document model fits "product data that has variable attributes" because items can contain different attributes without needing schema migrations typical of relational databases. The requirement to retrieve items by Product ID maps naturally to DynamoDB's primary key access pattern. The requirement for flexible queries on Category and Brand is met by creating global secondary indexes (GSIs) on those attributes so queries can be served efficiently without scanning the whole table.
Option B (Aurora) can scale reads, but it is not typically the best fit for sustained single-digit millisecond performance during sudden spikes without careful capacity planning. Option C is optimized for search and text
/query relevance rather than primary-key transactional access patterns. Option D uses Athena (interactive SQL over S3) which is not designed for millisecond-latency, high-QPS query workloads.

NEW QUESTION # 226
A retail company stores order information in an Amazon Aurora table named Orders. The company needs to create operational reports from the Orders table with minimal latency. The Orders table contains billions of rows, and over 100,000 transactions can occur each second.
A marketing team needs to join the Orders data with an Amazon Redshift table named Campaigns in the marketing team's data warehouse. The operational Aurora database must not be affected.
Which solution will meet these requirements with the LEAST operational effort?

A. Use AW5 Database Migration Service (AWS DMS) Serverless to replicate the Orders table to Amazon Redshift. Create a materialized view in Amazon Redshift to join with the Campaigns table.
B. Use the Aurora zero-ETL integration with Amazon Redshift to replicate the Orders table. Create a materialized view in Amazon Redshift to join with the Campaigns table.
C. Use federated queries to query the Orders table directly from Aurora. Create a materialized view in Amazon Redshift to join with the Campaigns table.
D. Use AWS Glue to replicate the Orders table to Amazon Redshift. Create a materialized view in Amazon Redshift to join with the Campaigns table.

Answer: D

NEW QUESTION # 227
A company needs to build an extract, transform, and load (ETL) pipeline that has separate stages for batch data ingestion, transformation, and storage. The pipeline must store the transformed data in an Amazon S3 bucket. Each stage must automatically retry failures. The pipeline must provide visibility into the success or failure of individual stages.
Which solution will meet these requirements with the LEAST operational overhead?

A. Schedule Apache Airflow directed acyclic graphs (DAGs) on Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate pipeline steps. Use Amazon Simple Queue Service (Amazon SQS) to ingest data. Use AWS Glue jobs to transform data and store the data in the S3 bucket.
B. Chain AWS Glue jobs that perform each stage together by using job triggers. Set the MaxRetries field to 0.
C. Build an Amazon EventBridge-based pipeline that invokes AWS Lambda functions to perform each stage.
D. Deploy AWS Step Functions workflows to orchestrate AWS Lambda functions that ingest data. Use AWS Glue jobs to transform the data and store the data in the S3 bucket.

Answer: D

Explanation:
AWS Step Functions is a fully managed, serverless orchestration service designed to coordinate multiple AWS services into reliable workflows. Step Functions natively provide automatic retries, error handling, state-level visibility, and execution history, which directly satisfy the requirements for retry logic and monitoring individual pipeline stages.
In this solution, AWS Lambda functions are used for batch data ingestion, AWS Glue jobs handle transformation, and Amazon S3 is used for storage. Step Functions orchestrate these stages while maintaining clear separation of responsibilities. Each step can be configured with retry and catch policies, allowing the workflow to automatically recover from transient failures without manual intervention.
Chaining AWS Glue jobs alone does not provide detailed stage-level visibility or flexible retry control, and setting MaxRetries to 0 contradicts the retry requirement. EventBridge-based pipelines lack native workflow state tracking. Amazon MWAA introduces unnecessary operational overhead, including environment management and Airflow maintenance.
Therefore, using AWS Step Functions to orchestrate Lambda and Glue provides the most scalable, observable, and low-maintenance solution.

NEW QUESTION # 228
A data engineer must orchestrate a series of Amazon Athena queries that will run every day. Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

A. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.
B. Create an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the Athena query has finished using the Athena Boto3 get_query_execution API call. Configure the workflow to invoke the next query when the current query has finished running.
C. Use an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running.
D. Use an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
E. Use an AWS Glue Python shell job and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.

Answer: B,D

Explanation:
Option A and B are the correct answers because they meet the requirements most cost-effectively. Using an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically is a simple and scalable way to orchestrate the queries. Creating an AWS Step Functions workflow and adding two states to check the query status and invoke the next query is a reliable and efficient way to handle the long-running queries.
Option C is incorrect because using an AWS Glue Python shell job to invoke the Athena queries programmatically is more expensive than using a Lambda function, as it requires provisioning and running a Glue job for each query.
Option D is incorrect because using an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully is not a cost- effective or reliable way to orchestrate the queries, as it wastes resources and time.
Option E is incorrect because using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch is an overkill solution that introduces unnecessary complexity and cost, as it requires setting up and managing an Airflow environment and an AWS Batch compute environment.
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.2: AWS Lambda, Section 5.3: AWS Step Functions, Pages 125-135 Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.1: AWS Lambda, Lesson 5.2: AWS Step Functions, Pages 1-15 AWS Documentation Overview, AWS Lambda Developer Guide, Working with AWS Lambda Functions, Configuring Function Triggers, Using AWS Lambda with Amazon Athena, Pages 1-4 AWS Documentation Overview, AWS Step Functions Developer Guide, Getting Started, Tutorial: Create a Hello World Workflow, Pages 1-8

NEW QUESTION # 229
A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.
The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.
Which solution will meet these requirements?

A. Create an IAM user that has an access key to access the DynamoDB table. Use Kubernetes secrets that are mounted in a volume of the EKS cluster nodes to store the user access key data.
B. Store the AWS credentials in an Amazon S3 bucket. Grant the EKS containers access to the S3 bucket to retrieve the credentials.
C. Attach an IAM role to the EKS worker nodes. Grant the IAM role access to DynamoDB. Use the IAM role to set up IAM roles service accounts (IRSA) functionality.
D. Create an IAM user that has an access key to access the DynamoDB table. Use environment variables in the EKS containers to store the IAM user access key data.

Answer: C

Explanation:
In this scenario, the company is using Amazon Elastic Kubernetes Service (EKS) and wants secure access to DynamoDB without embedding credentials inside the application containers. The best practice is to use IAM roles for service accounts (IRSA), which allows assigning IAM roles to Kubernetes service accounts. This lets the EKS pods assume specific IAM roles securely, without the need to store credentials in containers.
* IAM Roles for Service Accounts (IRSA):
* With IRSA, each pod in the EKS cluster can assume an IAM role that grants access to DynamoDB without needing to manage long-term credentials. The IAM role can be attached to the service account associated with the pod.
* This ensures least privilege access, improving security by preventing credentials from being embedded in the containers.

NEW QUESTION # 230
......

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