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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Data Operations and Support	22%	- Troubleshoot data workflow issues - Monitor and maintain data pipelines
Data Store Management	26%	- Select appropriate data storage solutions - Optimize storage performance and cost
Data Security and Governance	18%	- Apply governance and compliance best practices - Implement data security controls
Data Ingestion and Transformation	34%	- Build and manage data pipelines - Ingest and transform data using AWS services

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최신 AWS Certified Data Engineer Data-Engineer-Associate 무료샘플문제 (Q304-Q309):

질문 # 304
A company has a data processing pipeline that includes several dozen steps. The data processing pipeline needs to send alerts in real time when a step fails or succeeds. The data processing pipeline uses a combination of Amazon S3 buckets, AWS Lambda functions, and AWS Step Functions state machines.
A data engineer needs to create a solution to monitor the entire pipeline.
Which solution will meet these requirements?

A. Configure the Step Functions state machines to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket.
B. Configure the AWS Lambda functions to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket.
C. Use AWS CloudTrail to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications when a state machine fails to run or succeeds to run.
D. Configure an Amazon EventBridge rule to react when the execution status of a state machine changes.Configure the rule to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications.

정답：D

설명：
AWS Step Functions natively emitsstate change eventstoAmazon EventBridge, which can trigger an Amazon SNS notification. This is the most direct and real-time way to alert on success/failure without relying on custom logging or polling.
"Step Functions automatically emits status changes that EventBridge can capture to trigger alerts or workflows. Use EventBridge to invoke an SNS topic for real-time alerts on job status."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This provides real-time alerting and the least operational overhead.

질문 # 305
A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?

A. Use Amazon EMR and Apache Ranger.
B. Use a metastore on an Amazon RDS for MySQL DB instance.
C. Use a Hive metastore on an EMR cluster.
D. Use the AWS Glue Data Catalog.

정답：D

설명：
The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog that provides a central metadata repository for various data sources and formats. You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMR and Amazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWS Glue crawlers to automatically discover and catalog the metadata from Hive, and use the AWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as the Hive metastore. The other options are either more complex or require additional steps, such as setting up Apache Ranger for security, managing a Hive metastore on an EMR cluster or an RDS instance, or migrating the metadata manually. References:
Using the AWS Glue Data Catalog as the metastore for Hive (Section: Specifying AWS Glue Data Catalog as the metastore) Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue Data Catalog) AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadata from an existing Hive metastore) AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 131)

질문 # 306
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 AWS Glue ETL jobs to process the data stream.
C. Configure an AWS Lambda function to process the data stream.
D. Schedule Apache Spark jobs on an Amazon EMR cluster to process the data stream.

정답：A

설명：
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.

질문 # 307
A company wants to combine data from multiple software as a service (SaaS) applications for analysis.
A data engineering team needs to use Amazon QuickSight to perform the analysis and build dashboards. A data engineer needs to extract the data from the SaaS applications and make the data available for QuickSight queries.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Use Amazon AppFlow to create a Row for each SaaS application. Set an Amazon S3 bucket as the destination. Schedule the flows to extract the data to the bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.
B. Export data the from the SaaS applications as Microsoft Excel files. Create a data source and a dataset in QuickSight by uploading the Excel files.
C. Create AWS Lambda functions that call the required APIs to extract the data from the applications.
Store the data in an Amazon S3 bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight
D. Use AWS Lambda functions as Amazon Athena data source connectors to run federated queries against the SaaS applications. Create an Athena data source and a dataset in QuickSight.

정답：A

질문 # 308
A retail company is expanding its operations globally. The company needs to use Amazon QuickSight to accurately calculate currency exchange rates for financial reports. The company has an existing dashboard that includes a visual that is based on an analysis of a dataset that contains global currency values and exchange rates.
A data engineer needs to ensure that exchange rates are calculated with a precision of four decimal places. The calculations must be precomputed. The data engineer must materialize results in QuickSight super-fast, parallel, in-memory calculation engine (SPICE).
Which solution will meet these requirements?

A. Define and create the calculated field in the dashboard.
B. Define and create the calculated field in the dataset.
C. Define and create the calculated field in the analysis.
D. Define and create the calculated field in the visual.

정답：B

질문 # 309
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