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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Store Management | 26% | - Understand data cataloging
|
| Topic 2: Data Ingestion and Transformation | 34% | - Perform data ingestion
|
| Topic 3: Data Operations and Support | 22% | - Manage and troubleshoot data processes
|
| Topic 4: Data Security and Governance | 18% | - Ensure data encryption
|
>> Data-Engineer-Associate日本語関連対策 <<
今日、AmazonのData-Engineer-Associate認定試験は、IT業界で多くの人に重視されています、それは、IT能力のある人の重要な基準の目安となっています。多くの人はAmazonのData-Engineer-Associate試験への準備に悩んでいます。この記事を読んだあなたはラッキーだと思います。あなたは最高の方法を探しましたから。私たちの強力なIt-Passportsチームの開発するAmazonのData-Engineer-Associateソフトを使用して試験に保障があります。まだ躊躇？最初に私たちのソフトウェアのデモを無料でダウンロードしよう。
質問 # 290
A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes.
A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake.
Which solution will capture the changed data MOST cost-effectively?
正解：C
解説：
An open source data lake format, such as Apache Parquet, Apache ORC, or Delta Lake, is a cost-effective way to perform a change data capture (CDC) operation on semi-structured data stored in Amazon S3. An open source data lake format allows you to query data directly from S3 using standard SQL, without the need to move or copy data to another service. An open source data lake format also supports schema evolution, meaning it can handle changes in the data structure over time. An open source data lake format also supports upserts, meaning it can insert new data and update existing data in the same operation, using a merge command. This way, you can efficiently capture the changes from the data source and apply them to the S3 data lake, without duplicating or losing any data.
The other options are not as cost-effective as using an open source data lake format, as they involve additional steps or costs. Option A requires you to create and maintain an AWS Lambda function, which can be complex and error-prone. AWS Lambda also has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the CDC operation. Option B and D require you to ingest the data into a relational database service, such as Amazon RDS or Amazon Aurora, which can be expensive and unnecessary for semi-structured data. AWS Database Migration Service (AWS DMS) can write the changed data to the data lake, but it also charges you for the data replication and transfer. Additionally, AWS DMS does not support JSON as a source data type, so you would need to convert the data to a supported format before using AWS DMS. References:
* What is a data lake?
* Choosing a data format for your data lake
* Using the MERGE INTO command in Delta Lake
* [AWS Lambda quotas]
* [AWS Database Migration Service quotas]
質問 # 291
A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers. The data stream runs in Account A. The company wants to use an AWS Lambda function in Account B to process the data from the stream. The company creates a Lambda execution role in Account B that has permissions to access data from the stream in Account A.
What additional step must the company take to meet this requirement?
正解：D
解説：
To allow cross-account access to a Kinesis Data Stream, you must add a resource-based policy to the Kinesis stream in Account A, explicitly granting the Lambda execution role in Account B the required permissions.
* SCPs (A & C) set permissions boundaries, but do not grant access.
* Option D incorrectly refers to the Lambda function - but the Kinesis resource must allow access.
"You must add a resource-based policy to the Kinesis Data Stream in Account A to allow a Lambda function in Account B to consume from the stream." Reference: AWS Documentation - Cross-account Lambda access to Kinesis
質問 # 292
A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Which solution will meet this requirement MOST cost-effectively?
正解：C
解説：
Amazon Athena Federated Query is a feature that allows you to query data from multiple sources using standard SQL. You can use Athena Federated Query to join data from Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3, as well as other data sources such as MongoDB, Apache HBase, and Apache Kafka1. Athena Federated Query is a serverless and interactive service, meaning you do not need to provision or manage any infrastructure, and you only pay for the amount of data scanned by your queries.
Athena Federated Query is the most cost-effective solution for performing a one-time analysis job on data from multiple sources, as it eliminates the need to copy or move data, and allows you to query data directly from the source.
The other options are not as cost-effective as Athena Federated Query, as they involve additional steps or costs. Option A requires you to provision and pay for an Amazon EMR cluster, which can be expensive and time-consuming for a one-time job. Option B requires you to copy or move data from DynamoDB, RDS, and Redshift to S3, which can incur additional costs for data transfer and storage, and also introduce latency and complexity. Option D requires you to have an existing Redshift cluster, which can be costly and may not be necessary for a one-time job. Option D also does not support querying data from RDS directly, so you would need to use Redshift Federated Query to access RDS data, which adds another layer of complexity2. References:
Amazon Athena Federated Query
Redshift Spectrum vs Federated Query
質問 # 293
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：C
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. Reference:
Query editor V2
Working with materialized views
Scheduling queries
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]
質問 # 294
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.
正解：A
解説：
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
質問 # 295
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