Data-Engineer-Associate Fragen Antworten - Data-Engineer-Associate Prüfungsübungen

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Data-Engineer-Associate Prüfungsübungen - Data-Engineer-Associate Echte Fragen

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q139-Q144):

139. Frage
A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time.
The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.
B. Use Amazon Simple Queue Service(Amazon SQS) to buffer incomingsensor data. Use AWS Glue to store thedata in Amazon RDS for querying.
C. Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.
D. Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.

Antwort: C

140. Frage
A data engineer is launching an Amazon EMR cluster. The data that the data engineer needs to load into the new cluster is currently in an Amazon S3 bucket. The data engineer needs to ensure that data is encrypted both at rest and in transit.
The data that is in the S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The data engineer has an Amazon S3 path that has a Privacy Enhanced Mail (PEM) file.
Which solution will meet these requirements?

A. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Create a second security configuration. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach both security configurations to the cluster.
B. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.
C. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption.Create the EMR cluster, and attach the security configuration to the cluster.
D. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for local disk encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.

Antwort: B

Begründung:
To meet both encryption at rest and in transit, a single Amazon EMR security configuration can be created specifying the AWS KMS key for encryption at rest and the PEM file for in-transit encryption. The study guide clearly states:
"AWS Key Management Service (KMS) provides encryption for data at rest, and SSL/TLS ensures encryption for data in transit, providing end-to-end encryption within an AWS environment."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf A single security configuration is sufficient and the cleanest way to apply these security features during EMR cluster setup.

141. Frage
A company needs to implement a workflow to process transactions. Each transaction goes through multiple levels of validation. Each validation level depends on the preceding validation level.
The workflow must either process or reject each transaction within 24 hours. The workflow must run for less than 24 hours total.
Which solution will meet these requirements with the LEAST operational cost?

A. Create an express workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
B. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to implement the workflow.
C. Use AWS Lambda functions to implement the workflow. Use Amazon EventBridge to invoke the validation steps.
D. Create a standard workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.

Antwort: D

Begründung:
Option A is correct because this is a sequential, dependent workflow where each validation stage depends on the previous one, and the overall execution can last up to 24 hours. AWS states that Standard Workflows in Step Functions are ideal for long-running, durable, auditable workflows and can run for up to one year. AWS also documents the Wait for a Callback with Task Token pattern, which is specifically designed for workflows that must pause until an external process reports completion. That maps directly to multi-level validations that may complete asynchronously but still need orchestration within a bounded window.
Option B is incorrect because Express Workflows can run for only up to five minutes, so they cannot support a transaction workflow that may take many hours. Option C adds more custom code and operational management than Step Functions and does not naturally provide the same stateful orchestration, dependency tracking, and callback waiting pattern. Option D is also more operationally heavy and is better suited to data pipeline orchestration than per-transaction validation workflows. Therefore, Standard Step Functions provides the lowest operational cost while fully meeting the timing and dependency requirements.

142. Frage
A company uses an on-premises Microsoft SQL Server database to store financial transaction data. The company migrates the transaction data from the on-premises database to AWS at the end of each month. The company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL Server database has increased recently.
The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal downtown for the applications that access the database.
Which AWS service should the company use to meet these requirements?

A. AWS DataSync
B. AWS Database Migration Service (AWS DMS)
C. AWS Direct Connect
D. AWS Lambda

Antwort: B

Begründung:
AWS Database Migration Service (AWS DMS) is a cloud service that makes it possible to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores to AWS quickly, securely, and with minimal downtime and zero data loss1. AWS DMS supports migration between 20-plus database and analytics engines, such as Microsoft SQL Server to Amazon RDS for SQL Server2. AWS DMS takes overmany of the difficult or tedious tasks involved in a migration project, such as capacity analysis, hardware and software procurement, installation and administration, testing and debugging, and ongoing replication and monitoring1. AWS DMS is a cost-effective solution, as you only pay for the compute resources and additional log storage used during the migration process2. AWS DMS is the best solution for the company to migrate the financial transaction data from the on-premises Microsoft SQL Server database to AWS, as it meets the requirements of minimal downtime, zero data loss, and low cost.
Option A is not the best solution, as AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers, but it does not provide any built-in features for database migration.
You would have to write your own code to extract, transform, and load the data from the source to the target, which would increase the operational overhead and complexity.
Option C is not the best solution, as AWS Direct Connect is a service that establishes a dedicated network connection from your premises to AWS, but it does not provide any built-in features for database migration.
You would still need to use another service or tool to perform the actual data transfer, which would increase the cost and complexity.
Option D is not the best solution, as AWS DataSync is a service that makes it easy to transfer data between on-premises storage systems and AWS storage services, such as Amazon S3, Amazon EFS, and Amazon FSx for Windows File Server, but it does not support Amazon RDS for SQL Server as a target. You would have to use another service or tool to migrate the data from Amazon S3 to Amazon RDS for SQL Server, which would increase the latency and complexity. References:
Database Migration - AWS Database Migration Service - AWS
What is AWS Database Migration Service?
AWS Database Migration Service Documentation
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

143. Frage
A company uses Amazon Redshift to store order transactions from the current day. The company has an orders table that contains the previous order data. The company also has a staging table that contains new or updated order records. The company needs to remove stale records from the orders table and insert the most recent data in the orders table from the staging table. Several downstream applications need the orders table to display up-to-date information.
Which solution will meet these requirements?

A. Write an Amazon Redshift stored procedure that deletes the stale records from the orders table and inserts new records from the staging table.
B. Use Amazon Redshift Spectrum to delete stale records from the orders table and insert records from the staging table into the orders table.
C. Unload the orders table and the staging table to Amazon S3. Delete stale orders table data and insert new staging table data in Amazon S3 by using Amazon Athena. Copy the orders S3 table to the orders Amazon Redshift table.
D. Use Amazon Athena federated queries to read stale records from the orders table. Delete the stale records and insert the records from the staging table into the orders table.

Antwort: A

Begründung:
Option D is correct because Amazon Redshift stored procedures are designed to encapsulate a sequence of SQL statements and business logic inside the database. AWS documentation states that stored procedures are commonly used for data transformation, data validation, and business-specific logic, and that they can combine multiple SQL steps into one procedure. AWS also documents the standard Redshift pattern for deleting stale rows and inserting fresh rows from a staging table, which is exactly the requirement here.
Keeping the operation inside Redshift is the most direct way to maintain an up-to-date orders table for downstream consumers.
Option A is incorrect because Redshift Spectrum is for querying external data in S3, not for performing this in- place Redshift table-maintenance pattern. Option B adds unnecessary unload and reload steps, creating delay and operational complexity. Option C is also unsuitable because Athena federated queries are not the right mechanism for transactional maintenance of Redshift tables. The correct DEA-C01-style answer is to use Redshift-native procedural SQL to delete stale rows and insert current rows from staging.

144. Frage
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