2026 ExamPassdump 최신 Data-Engineer-Associate PDF 버전 시험 문제집과 Data-Engineer-Associate 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1U9GYmqNqsJ1e8G7RrFZmcCt-Kja27NTx
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
| Topic 2: Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Topic 3: Data Operations and Support | 22% | - Monitor and maintain data pipelines - Troubleshoot data workflow issues |
| Topic 4: Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
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질문 # 223
A mobile gaming company wants to capture data from its gaming app. The company wants to make the data available to three internal consumers of the data. The data records are approximately 20 KB in size.
The company wants to achieve optimal throughput from each device that runs the gaming app. Additionally, the company wants to develop an application to process data streams. The stream-processing application must have dedicated throughput for each internal consumer.
Which solution will meet these requirements?
정답：D
설명：
* Problem Analysis:
* Input Requirements: Gaming app generates approximately20 KB data records, which must be ingested and made available tothree internal consumerswithdedicated throughput.
* Key Requirements:
* High throughput for ingestion from each device.
* Dedicated processing bandwidth for each consumer.
* Key Considerations:
* Amazon Kinesis Data Streamssupports high-throughput ingestion withPutRecords APIfor batch writes.
* TheEnhanced Fan-Outfeature providesdedicated throughputto each consumer, avoiding bandwidth contention.
* This solution avoids bottlenecks and ensures optimal throughput for the gaming application and consumers.
* Solution Analysis:
* Option A: Kinesis Data Streams + Enhanced Fan-Out
* PutRecords API is designed for batch writes, improving ingestion performance.
* Enhanced Fan-Out allows each consumer to process the stream independently with dedicated throughput.
* Option B: Data Firehose + Dedicated Throughput Request
* Firehose is not designed for real-time stream processing or fan-out. It delivers data to destinations like S3, Redshift, or OpenSearch, not multiple independent consumers.
* Option C: Data Firehose + Enhanced Fan-Out
* Firehose does not support enhanced fan-out. This option is invalid.
* Option D: Kinesis Data Streams + EC2 Instances
* Hosting stream-processing applications on EC2 increases operational overhead compared to native enhanced fan-out.
* Final Recommendation:
* UseKinesis Data Streams with Enhanced Fan-Outfor high-throughput ingestion and dedicated consumer bandwidth.
:
Kinesis Data Streams Enhanced Fan-Out
PutRecords API for Batch Writes
질문 # 224
A company is developing machine learning (ML) models. A data engineer needs to apply data quality rules to training data. The company stores the training data in an Amazon S3 bucket.
정답：A
설명：
AWS Glue DataBrew provides a no-code way to define and run data quality rulesets for data stored in S3.
You can trigger profiling jobs via Amazon EventBridge on new uploads for automated checks.
"Use AWS Glue DataBrew to define and run data quality rules on S3 datasets with minimal coding effort.
Automate validation by triggering jobs through EventBridge."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
질문 # 225
A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?
정답：A
설명：
The query editor v2 in Amazon Redshift is a web-based tool that allows users to run SQL queries and scripts on Amazon Redshift clusters. The query editor v2 supports creating and managing materialized views, which are precomputed results of a query that can improve the performance of subsequent queries. The query editor v2 also supports scheduling queries to run at specified intervals, which can be used to refresh materialized views automatically. This solution requires the least effort, as it does not involve any additional services, coding, or configuration. The other solutions are more complex and require more operational overhead.
Apache Airflow is an open-source platform for orchestrating workflows, which can be used to refresh materialized views, but it requires setting up and managing an Airflow environment, creating DAGs (directed acyclic graphs) to define the workflows, and integrating with Amazon Redshift. AWS Lambda is a serverless compute service that can run code in response to events, which can be used to refresh materialized views, but it requires creating and deploying Lambda functions, defining UDFs within Amazon Redshift, and triggering the functions using events or schedules. AWS Glue is a fully managed ETL service that can run jobs to transform and load data, which can be used to refresh materialized views, but it requires creating and configuring Glue jobs, defining Glue workflows to orchestrate the jobs, and scheduling the workflows using triggers. References:
* Query editor V2
* Working with materialized views
* Scheduling queries
* [AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]
질문 # 226
A data engineer needs to query data from multiple sources to generate an annual report. The analytics team uses Amazon Redshift for analysis. The data engineer needs to integrate Amazon Redshift data with 10 years of historical data from Amazon RDS for PostgreSQL and RDS for MySQL. All the databases are in the same VPC. The data engineer needs a solution that provides seamless data integration with Amazon Redshift.
Which solution will meet these requirements in the MOST cost-effective way?
정답：D
설명：
Option A is the most cost-effective because it enables seamless integration by allowing analysts to access and join external relational data directly from within Amazon Redshift for reporting, without building and operating a separate ingestion pipeline for a one-time (annual) workload. The study material frames Amazon Redshift as the centralized analytics store for complex queries and reporting, making it the natural place to perform the final transformations and analysis.
The alternatives introduce extra operational steps and recurring costs. Option B requires exporting large historical datasets to S3 and then loading them into Redshift, which increases data movement and operational complexity. Option C adds ETL job development, scheduling, retries, and monitoring overhead that is unnecessary if the primary goal is integrated querying for an annual report. Option D (DMS) is best when you need ongoing migration or continuous replication; it is typically more operationally heavy than needed for
"query across sources" use cases and still requires additional setup and maintenance.
Because all databases are already in the same VPC and the analytics platform is Redshift, federated querying provides the most direct, lowest-operations path to integrate and analyze the data where it already needs to be consumed.
질문 # 227
A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated.
A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data.
Which solution will meet this requirement?
정답：D
설명：
Amazon EC2 instances can use two types of storage volumes: instance store volumes and Amazon EBS volumes. Instance store volumes are ephemeral, meaning they are only attached to the instance for the duration of its life cycle. If the instance is stopped, terminated, or fails, the data on the instance store volume is lost. Amazon EBS volumes are persistent, meaning they can be detached from the instance and attached to another instance, and the data on the volume is preserved. To meet the requirement of persisting the data even if the EC2 instances are terminated, the data engineer must use Amazon EBS volumes to store the application data. The solution is to launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume, which is the default option for most AMIs. Then, the data engineer must attach an Amazon EBS volume to each instance and configure the application to write the data to the EBS volume. This way, the data will be saved on the EBS volume and can be accessed by another instance if needed. The data engineer can apply the default settings to the EC2 instances, as there is no need to modify the instance type, security group, or IAM role for this solution. The other options are either not feasible or not optimal. Launching new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data (option A) or by using an AMI that is backed by a root Amazon EBS volume that contains the application data (option B) would not work, as the data on the AMI would be outdated and overwritten by the new instances. Attaching an additional EC2 instance store volume to contain the application data (option D) would not work, as the data on the instance store volume would be lost if the instance is terminated. Reference:
Amazon EC2 Instance Store
Amazon EBS Volumes
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.1: Amazon EC2
질문 # 228
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