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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Data Store Management	26%	- Optimize storage performance and cost - Design and implement data storage solutions 1. S3, Redshift, DynamoDB, RDS, Lake Formation 2. Data lakes, data warehouses, databases - Manage data lifecycle and storage tiers
Data Operations and Support	22%	- Backup, restore, and disaster recovery - Monitor and troubleshoot data pipelines 1. CloudWatch, X-Ray, logging and metrics - Ensure reliability and scalability - Automate operational tasks
Data Ingestion and Transformation	34%	- Transform and enrich data 1. Use Spark, EMR, Step Functions 2. Orchestrate data pipelines 3. Apply data processing logic - Ingest data from various sources 1. Use services like Kinesis, DMS, Glue, S3 2. Batch and streaming data ingestion - Implement data quality and validation
Data Security and Governance	18%	- Implement access control and authentication 1. IAM, Lake Formation, resource policies - Encrypt data at rest and in transit - Enforce compliance and data governance 1. Data lineage, audit logging, regulatory requirements - Protect sensitive data

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Data-Engineer-Associate資格問題集 & Data-Engineer-Associate試験

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) 認定 Data-Engineer-Associate 試験問題 (Q119-Q124):

質問 # 119
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 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.
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. 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.
D. 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.

正解：B

解説：
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.

質問 # 120
A data engineer needs to analyze time-sensitive sales data. The company stores the data in an Amazon S3 bucket. The data engineer uses AWS Glue Data Catalog to access the data.
When performing the analysis, the data engineer notices that some records are missing or out of date.
What is the likely cause of these issues?

A. The AWS Glue job schedules overlap with one another.
B. AWS Glue Data Catalog is not up to date with the latest S3 partition changes.
C. Incorrect IAM roles are assigned to the AWS Glue jobs.
D. Versioning is not enabled on the S3 bucket.

正解：B

解説：
AWS Glue Data Catalog stores metadata about data stored in Amazon S3, including table schemas and partitions. If new partitions are added to S3 and the Glue Data Catalog is not updated-either through crawlers or explicit API calls-query engines such as Amazon Athena and AWS Glue will not be aware of the new data.
This leads to analysis results that appear incomplete or outdated, even though the underlying data exists in S3.
This is a common issue in time-sensitive analytics workloads where data arrives frequently and partitions change often.
IAM permission issues would typically cause access failures rather than missing records. S3 versioning affects object history, not query visibility. Overlapping Glue job schedules could cause processing delays but would not directly cause missing data during query time.
Therefore, the most likely cause is that the Glue Data Catalog metadata is not synchronized with the latest S3 partitions.

質問 # 121
A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.
A data engineer must implement a solution that can detect the schema for these data sources. The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create a PvSpark proqram in AWS Lambda to extract, transform, and load the data into the S3 bucket.
B. Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket.
Create a pipeline in Apache Spark.
C. Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket.
Create a pipeline in Apache Spark.
D. Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.

正解：C

解説：
AWS Glue is a fully managed service that provides a serverless data integration platform. It can automatically discover and categorize data from various sources, including SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. It can also infer the schema of the data and store it in the AWS Glue Data Catalog, which is a central metadata repository. AWS Glue can then use the schema information to generate and run Apache Spark code to extract, transform, and load the data into an Amazon S3 bucket. AWS Glue can also monitor and optimize the performance and cost of the data pipeline, and handle any schema changes that may occur in the source data. AWS Glue can meet the SLA of loading the data into the S3 bucket within 15 minutes of data creation, as it can trigger the data pipeline based on events, schedules, or on-demand. AWS Glue has the least operational overhead among the options, as it does not require provisioning, configuring, or managing any servers or clusters. It also handles scaling, patching, and security automatically. References:
* AWS Glue
* [AWS Glue Data Catalog]
* [AWS Glue Developer Guide]
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

質問 # 122
A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer's original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data
WHERE year = 2023
GROUP BY product_name
How should the data engineer modify the Athena query to meet these requirements?

A. Add HAVING sumfsales amount) > 0 after the GROUP BY clause.
B. Remove the GROUP BY clause
C. Change WHERE year = 2023 to WHERE extractlyear FROM sales data) = 2023.
D. Replace sum(sales amount) with count(*J for the aggregation.

正解：C

解説：
The original query does not return results for all of the products because the year column in the sales_data table is not an integer, but a timestamp. Therefore, the WHERE clause does not filter the data correctly, and only returns the products that have a null value for the year column. To fix this, the data engineer should use the extract function to extract the year from the timestamp and compare it with 2023. This way, the querywill return the correct results for all of the products in the sales_data table. The other options are either incorrect or irrelevant, as they do not address the root cause of the issue. Replacing sum with count does not change the filtering condition, adding HAVING clause does not affect the grouping logic, and removing the GROUP BY clause does not solve the problem of missing products. References:
Troubleshooting JSON queries - Amazon Athena (Section: JSON related errors) When I query a table in Amazon Athena, the TIMESTAMP result is empty (Section: Resolution) AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 7, page 197)

質問 # 123
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 Apache Spark jobs on an Amazon EMR cluster to process the data stream.
C. Configure an AWS Lambda function to process the data stream.
D. Schedule AWS Glue ETL jobs to process the data stream.

正解：A

解説：
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.

質問 # 124
......

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