短時間で合格できちゃうAmazon Data-Engineer-Associateの問題集

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) 認定 Data-Engineer-Associate 試験問題 (Q112-Q117):

質問 # 112
A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools.
Which solution will meet these requirements with the LEAST operational overhead?

正解：A

解説：
This solution meets the requirements of implementing real-time analytics capabilities with the least operational overhead. By creating an external schema in Amazon Redshift, you can access the data from Kinesis Data Streams using SQL queries without having to load the data into the cluster. By creating a materialized view on top of the stream, you can store the results of the query in the cluster and make them available for analysis. By setting the materialized view to auto refresh, you can ensure that the view is updated with the latest data from the stream at regular intervals. This way, you can derive near real-time insights by using existing BI and analytics tools. References:
Amazon Redshift streaming ingestion
Creating an external schema for Amazon Kinesis Data Streams
Creating a materialized view for Amazon Kinesis Data Streams


質問 # 113
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?

正解：D


質問 # 114
A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand.
Which solution will meet these requirements?

正解：C

解説：
Option A is the correct design for single-digit millisecond latency with unpredictable spikes and variable attributes. The study material describes Amazon DynamoDB as a NoSQL database "designed for highly dynamic datasets with frequent read and write operations," providing low-latency performance at any scale
-which directly matches the latency and traffic-spike requirements.
DynamoDB's key-value and document model fits "product data that has variable attributes" because items can contain different attributes without needing schema migrations typical of relational databases. The requirement to retrieve items by Product ID maps naturally to DynamoDB's primary key access pattern.
The requirement for flexible queries on Category and Brand is met by creating global secondary indexes (GSIs) on those attributes so queries can be served efficiently without scanning the whole table.
Option B (Aurora) can scale reads, but it is not typically the best fit for sustained single-digit millisecond performance during sudden spikes without careful capacity planning. Option C is optimized for search and text/query relevance rather than primary-key transactional access patterns.
Option D uses Athena (interactive SQL over S3) which is not designed for millisecond-latency, high- QPS query workloads.


質問 # 115
A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account.
Which solution will meet these requirements?

正解：C

解説：
Athena workgroups are a way to isolate query execution and query history among users, teams, and applications that share the same AWS account. By creating a workgroup for each use case, the company can control the access and actions on the workgroup resource using resource-level IAM permissions or identity-based IAM policies. The company can also use tags to organize and identify the workgroups, and use them as conditions in the IAM policies to grant or deny permissions to the workgroup. This solution meets the requirements of separating query processes and access to query history among users, teams, and applications that are in the same AWS account. Reference:
Athena Workgroups
IAM policies for accessing workgroups
Workgroup example policies


質問 # 116
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

正解：B

解説：
Concurrency scaling is a feature that allows you to support thousands of concurrent users and queries, with consistently fast query performance. When you turn on concurrency scaling, Amazon Redshift automatically adds query processing power in seconds to process queries without any delays. You can manage which queries are sent to the concurrency-scaling cluster by configuring WLM queues. To turn on concurrency scaling for a queue, set the Concurrency Scaling mode value to auto. The other options are either incorrect or irrelevant, as they do not enable concurrency scaling for the existing Redshift cluster on RA3 nodes. References:
Working with concurrency scaling - Amazon Redshift
Amazon Redshift Concurrency Scaling - Amazon Web Services
Configuring concurrency scaling queues - Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 6, page 163)


質問 # 117
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