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Amazon Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Data Ingestion and Transformation34%- Transform and process data
  • 1. Batch and stream processing
  • 2. Data partitioning and compression
  • 3. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda)
  • 4. ETL/ELT patterns
- Apply programming concepts
  • 1. SQL, Python, Scala
  • 2. Version control
  • 3. Infrastructure as Code (IaC)
- Orchestrate data pipelines
  • 1. AWS Step Functions
  • 2. Amazon Managed Workflows for Apache Airflow (MWAA)
  • 3. Event-driven architectures
  • 4. AWS Glue Workflows
- Perform data ingestion
  • 1. Batch data ingestion (scheduled ingestion, event-driven ingestion)
  • 2. Data ingestion patterns (frequency and data history)
  • 3. Throughput and latency characteristics for AWS services
  • 4. Replayability of data
  • 5. Streaming data ingestion
Data Security and Governance18%- Apply authentication and authorization
  • 1. Service control policies (SCPs)
  • 2. Amazon S3 bucket policies
  • 3. AWS IAM policies and roles
- Manage data privacy and compliance
  • 1. AWS Lake Formation permissions
  • 2. Data masking and tokenization
  • 3. PII data handling
- Implement data quality checks
  • 1. Data validation
  • 2. AWS Glue DataBrew
- Ensure data encryption
  • 1. Encryption at rest and in transit
  • 2. AWS KMS
Data Operations and Support22%- Monitor data pipelines
  • 1. AWS CloudTrail
  • 2. Amazon CloudWatch
  • 3. Logging and metrics
- Automate data pipelines
  • 1. AWS Lambda triggers
  • 2. Event-driven triggers
  • 3. Scheduling jobs
- Manage and troubleshoot data processes
  • 1. Performance tuning
  • 2. Cost optimization
  • 3. Debugging failed jobs
Data Store Management26%- Design data models
  • 1. Normalization and denormalization
  • 2. Schema design
  • 3. Partitioning and indexing strategies
- Manage data lifecycle
  • 1. Amazon S3 storage classes
  • 2. Data retention policies
  • 3. Data archiving
- Choose a data store
  • 1. Data characteristics (structured, semi-structured, unstructured)
  • 2. Data lakes vs. data warehouses
  • 3. Access and storage patterns
  • 4. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift
- Understand data cataloging
  • 1. Schema evolution
  • 2. Data discovery and classification
  • 3. AWS Glue Data Catalog

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q236-Q241):

NEW QUESTION # 236
A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size.
A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file.
Which solution will meet this requirement with the LEAST operational effort?

Answer: D


NEW QUESTION # 237
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Select TWO.)

Answer: A,E


NEW QUESTION # 238
A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day.
Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A business intelligence team queries the tables between 10 AM and 8 PM every day.
Which solution will meet these requirements in the MOST operationally efficient way?

Answer: D

Explanation:
The company needs to load data warehouse tables into Amazon S3 and perform incremental synchronization with daily updates. The most efficient solution is to use AWS Database Migration Service (AWS DMS) with a combination of full load and change data capture (CDC) to handle the initial load and daily incremental updates.
* Option A: Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.DMS is designed to migrate databases to AWS, and the combination of full load plus CDC is ideal for handling incremental data changes efficiently. AWS Glue can then be used to append the incremental data to the full data set in S3. This solution is highly operationally efficient because it automates both the full load and incremental updates.
Options B, C, and D are less operationally efficient because they either require writing custom logic to handle bookmarks manually or involve unnecessary daily full loads.
References:
* AWS Database Migration Service Documentation
* AWS Glue Documentation


NEW QUESTION # 239
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?

Answer: D

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.
Reference:
Alternatives Considered:
A (Storing AWS credentials in S3): Storing AWS credentials in S3 and retrieving them introduces security risks and violates the principle of not embedding credentials.
C (IAM user access keys in environment variables): This also embeds credentials, which is not recommended.
D (Kubernetes secrets): Storing user access keys as secrets is an option, but it still involves handling long-term credentials manually, which is less secure than using IRSA.
IAM Best Practices for Amazon EKS
Secure Access to DynamoDB from EKS


NEW QUESTION # 240
A ride-sharing company stores records for all rides in an Amazon DynamoDB table. The table includes the following columns and types of values:
RideID | RiderID | DriverID | RideStatus | TripStartTime | TripEndTime
XA1231 | AXEF1 | BN123 | Active | 2025-02-11 | NULL
XA1232 | AXEF2 | BN124 | Completed | 2025-02-11 | 2025-02-11
The table currently contains billions of items. The table is partitioned by RideID and uses TripStartTime as the sort key. The company wants to use the data to build a personal interface to give drivers the ability to view the rides that each driver has completed, based on RideStatus. The solution must access the necessary data without scanning the entire table.
Which solution will meet these requirements?

Answer: D

Explanation:
Option C is correct because the required access pattern is: find all completed rides for a specific driver. In DynamoDB, when you need to query data efficiently by attributes that are not part of the base table primary key, you typically create a global secondary index (GSI). AWS documentation states that a GSI can have a key schema that is different from the base table and can use top-level attributes such as DriverID and RideStatus as its partition and sort keys. That makes it possible to query directly for a given driver and then narrow the results to completed rides, without scanning the full table.
Option A is incorrect because a local secondary index (LSI) must use the same partition key as the base table.
Since the table partition key is RideID, an LSI cannot be created with DriverID as the partitioning access path.
Option B uses RiderID, which does not satisfy the requirement to retrieve rides by driver. Option D is also wrong because filter expressions are applied after items are read and therefore do not avoid scanning large amounts of data. The study guide emphasizes choosing the correct data model and access pattern for the workload, which places this question in the Data Storage and Management domain.


NEW QUESTION # 241
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