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人間ができるというのは、できることを信じるからです。MogiExamは IT職員を助けられるのは職員の能力を証明することができるからです。MogiExamのAmazonのData-Engineer-Associate「AWS Certified Data Engineer - Associate (DEA-C01)」試験はあなたが成功することを助けるトレーニング資料です。AmazonのData-Engineer-Associate認定試験に受かりたいのなら、MogiExam を選んでください。時には、成功と失敗の距離は非常に短いです。前へ何歩進んだら成功できます。あなたはどうしますか。前へ進みたくないですか。MogiExamは成功の扉ですから、 MogiExamを利用してください。
| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Design and implement data storage solutions
- Optimize storage performance and cost |
| Data Ingestion and Transformation | 34% | - Implement data quality and validation - Transform and enrich data
|
| Data Operations and Support | 22% | - Monitor and troubleshoot data pipelines
- Ensure reliability and scalability - Backup, restore, and disaster recovery |
| Data Security and Governance | 18% | - Implement access control and authentication
- Protect sensitive data |
>> Data-Engineer-Associate最速合格 <<
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質問 # 229
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?
正解：B
解説：
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]
質問 # 230
The company stores a large volume of customer records in Amazon S3. To comply with regulations, the company must be able to access new customer records immediately for the first 30 days after the records are created. The company accesses records that are older than 30 days infrequently.
The company needs to cost-optimize its Amazon S3 storage.
Which solution will meet these requirements MOST cost-effectively?
正解：B
解説：
The most cost-effective solution in this case is to apply a lifecycle policy to transition records to Amazon S3 Standard-IA storage after 30 days. Here's why:
Amazon S3 Lifecycle Policies: Amazon S3 offers lifecycle policies that allow you to automatically transition objects between different storage classes to optimize costs. For data that is frequently accessed in the first 30 days and infrequently accessed after that, transitioning from the S3 Standard storage class to S3 Standard- Infrequent Access (S3 Standard-IA) after 30 days makes the most sense. S3 Standard-IA is designed for data that is accessed less frequently but still needs to be retained, offering lower storage costs than S3 Standard with a retrieval cost for access.
Cost Optimization: S3 Standard-IA offers a lower price per GB than S3 Standard. Since the data will be accessed infrequently after 30 days, using S3 Standard-IA will lower storage costs while still allowing for immediate retrieval when necessary.
Compliance with Regulations: Since the records need to be immediately accessible for the first 30 days, the use of S3 Standard for that period ensures compliance with regulatory requirements. After 30 days, transitioning to S3 Standard-IA continues to meet access requirements for infrequent access while reducing storage costs.
Alternatives Considered:
Option B (S3 Intelligent-Tiering): While S3 Intelligent-Tiering automatically moves data between access tiers based on access patterns, it incurs a small monthly monitoring and automation charge per object. It could be a viable option, but transitioning data to S3 Standard-IA directly would be more cost-effective since the pattern of access is well-known (frequent for 30 days, infrequent thereafter).
Option C (S3 Glacier Deep Archive): Glacier Deep Archive is the lowest-cost storage class, but it is not suitable in this case because the data needs to be accessed immediately within 30 days and on an infrequent basis thereafter. Glacier Deep Archive requires hours for data retrieval, which is not acceptable for infrequent access needs.
Option D (S3 Standard-IA for all records): Using S3 Standard-IA for all records would result in higher costs for the first 30 days, as the data is frequently accessed. S3 Standard-IA incurs retrieval charges, making it less suitable for frequently accessed data.
Amazon S3 Lifecycle Policies
S3 Storage Classes
Cost Management and Data Optimization Using Lifecycle Policies
AWS Data Engineering Documentation
質問 # 231
A transportation company wants to track vehicle movements by capturing geolocation records. The records are 10 bytes in size. The company receives up to 10,000 records every second. Data transmission delays of a few minutes are acceptable because of unreliable network conditions.
The transportation company wants to use Amazon Kinesis Data Streams to ingest the geolocation data. The company needs a reliable mechanism to send data to Kinesis Data Streams. The company needs to maximize the throughput efficiency of the Kinesis shards.
Which solution will meet these requirements in the MOST operationally efficient way?
正解：C
解説：
Problem Analysis:
The company ingests geolocation records (10 bytes each) at 10,000 records per second into Kinesis Data Streams.
Data transmission delays are acceptable, but the solution must maximize throughput efficiency.
Key Considerations:
The Kinesis Producer Library (KPL) batches records and uses aggregation to optimize shard throughput.
Efficiently handles high-throughput scenarios with minimal operational overhead.
Solution Analysis:
Option A: Kinesis Agent
Designed for file-based ingestion; not optimized for geolocation records.
Option B: KPL
Aggregates records into larger payloads, significantly improving shard throughput.
Suitable for applications generating small, high-frequency records.
Option C: Kinesis Firehose
Firehose is for delivery to destinations like S3 or Redshift and is not optimized for direct ingestion to Kinesis Data Streams.
Option D: Kinesis SDK
The SDK lacks advanced features like aggregation, resulting in lower throughput efficiency.
Final Recommendation:
Use Kinesis Producer Library (KPL) for its built-in aggregation and batching capabilities.
Reference:
Kinesis Producer Library (KPL) Overview
質問 # 232
A company needs to store semi-structured transactional data in a serverless database.
The application writes data infrequently but reads it frequently, with millisecond retrieval required.
正解：D
解説：
Amazon DynamoDB is a serverless, low-latency, NoSQL database ideal for semi-structured data.
Adding DynamoDB Accelerator (DAX) provides microsecond response times for read-heavy workloads.
"For applications requiring millisecond or sub-millisecond reads with serverless operation, use DynamoDB with DAX caching."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
質問 # 233
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?
正解：A
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 # 234
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