Quiz 2026 Amazon Data-Engineer-Associate: First-grade AWS Certified Data Engineer - Associate (DEA-C01) Test Valid

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q108-Q113):

NEW QUESTION # 108
A company uses Amazon S3 buckets, AWS Glue tables, and Amazon Athena as components of a data lake. Recently, the company expanded its sales range to multiple new states. The company wants to introduce state names as a new partition to the existing S3 bucket, which is currently partitioned by date.
The company needs to ensure that additional partitions will not disrupt daily synchronization between the AWS Glue Data Catalog and the S3 buckets.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use the AWS Glue API to manually update the Data Catalog.
B. Run an MSCK REPAIR TABLE command in Athena.
C. Schedule an AWS Glue crawler to periodically update the Data Catalog.
D. Run a REFRESH TABLE command in Athena.

Answer: C

Explanation:
Scheduling an AWS Glue crawler to periodically update the Data Catalog automates the process of detecting new partitions and updating the catalog, which minimizes manual maintenance and operational overhead.

NEW QUESTION # 109
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.
The company receives 2 GB erf sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use Amazon Kinesis Data Streams to fetch sales records from the MySQL database. Use Amazon Managed Service for Apache Flink to correlate the datasets. Use AWS Step Functions to orchestrate the process.
B. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with the sales opportunities. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the process.
C. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with sales opportunities. Use AWS Step Functions to orchestrate the process.
D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to fetch both datasets. Use AWS Lambda functions to correlate the datasets. Use AWS Step Functions to orchestrate the process.

Answer: C

Explanation:
Problem Analysis:
The company processes 2 GB of daily sales records and 100 GB of Salesforce sales opportunities.
The goal is to analyze and correlate the two datasets with low operational overhead.
The process must run once nightly.
Key Considerations:
Amazon AppFlow simplifies data integration with Salesforce.
AWS Glue can extract data from MySQL and perform ETL operations.
Step Functions can orchestrate workflows with minimal manual intervention.
Apache Airflow and Flink add complexity, which conflicts with the requirement for low operational overhead.
Solution Analysis:
Option A: MWAA + Lambda + Step Functions
Requires custom Lambda code for dataset correlation, increasing development and operational complexity.
Option B: AppFlow + Glue + MWAA
MWAA adds orchestration overhead compared to the simpler Step Functions.
Option C: AppFlow + Glue + Step Functions
AppFlow fetches Salesforce data, Glue extracts MySQL data, and Step Functions orchestrate the entire process.
Minimal setup and operational overhead, making it the best choice.
Option D: AppFlow + Kinesis + Flink + Step Functions
Using Kinesis and Flink for batch processing introduces unnecessary complexity.
Final Recommendation:
Use Amazon AppFlow to fetch Salesforce data, AWS Glue to process MySQL data, and Step Functions for orchestration.
Reference:
Amazon AppFlow Overview
AWS Glue ETL Documentation
AWS Step Functions

NEW QUESTION # 110
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 environment variables in the EKS containers to store the IAM user access key data.
B. 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.
C. 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.
D. Store the AWS credentials in an Amazon S3 bucket. Grant the EKS containers access to the S3 bucket to retrieve the credentials.

Answer: B

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 # 111
A company has an application that is deployed on AWS. The application uses Amazon Simple Notification Service (Amazon SNS) with multiple topics. The company's security team needs to be able to audit all Publish and PublishBatch API actions for all the SNS topics. The company ' s application team and security team must also be able to query the audit data. The company has already established an event data store in AWS CloudTrail Lake to collect all events.
Which solution will meet these requirements with the LEAST operational overhead?

A. Enable management events for the SNS topics. Use CloudTrail Lake to query the audit data.
B. Enable data events for the SNS topics. Create a table in AWS Glue Data Catalog. Query the data by using Amazon Athena.
C. Enable data events for the SNS topics. Use CloudTrail Lake to query the audit data.
D. Enable management events for the SNS topics. Create a table in AWS Glue Data Catalog. Query the data by using Amazon Athena.

Answer: C

Explanation:
The correct answer is C because SNS Publish and PublishBatch operations are data-plane operations, not ordinary management events. AWS CloudTrail documentation specifically lists Amazon SNS Publish and PublishBatch API operations on topics as supported data events. Because the company already has a CloudTrail Lake event data store, the lowest-operational-overhead approach is to enable the relevant SNS data events and query them directly in CloudTrail Lake. Creating Glue Data Catalog tables and querying with Athena adds unnecessary cataloging and query infrastructure. Management events would not satisfy the requirement to audit the requested SNS publishing actions. Therefore, the precise configuration is SNS data events plus CloudTrail Lake querying.

NEW QUESTION # 112
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications? (Select TWO.)

A. Spark SQL
B. Gremlin
C. ANSI SQL
D. SQL
E. SPARQL

Answer: B,E

Explanation:
Amazon Neptune supports graph applications using Gremlin and SPARQL as query languages. Neptune is a fully managed graph database service that supports both property graph and RDF graph models.
* Option A: GremlinGremlin is a query language for property graph databases, which is supported by Amazon Neptune. It allows the traversal and manipulation of graph data in the property graph model.
* Option D: SPARQLSPARQL is a query language for querying RDF graph data in Neptune. It is used to query, manipulate, and retrieve information stored in RDF format.
Other options:
* SQL (Option B) and ANSI SQL (Option C) are traditional relational database query languages and are not used for graph databases.
* Spark SQL (Option E) is related to Apache Spark for big data processing, not for querying graph databases.
References:
* Amazon Neptune Documentation
* Gremlin Documentation
* SPARQL Documentation

NEW QUESTION # 113
......

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