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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion and Transformation | 34% | - Apply programming concepts
|
| Topic 2: Data Security and Governance | 18% | - Implement data quality checks
|
| Topic 3: Data Store Management | 26% | - Understand data cataloging
|
| Topic 4: Data Operations and Support | 22% | - Manage and troubleshoot data processes
|
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問題 #204
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?
答案：D
解題說明：
The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. Reference:
Using the Amazon Redshift Data API
Using Amazon EventBridge with Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift
問題 #205
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Select TWO.)
答案：C,E
解題說明：
The company needs to use machine learning models for real-time inventory recommendations and future inventory predictions while leveraging both Amazon Redshift and Amazon SageMaker.
Option A: Use Amazon Redshift ML to generate inventory recommendations.
Amazon Redshift ML allows you to build, train, and deploy machine learning models directly from Redshift using SQL statements. It integrates with SageMaker to train models and run inference. This feature is useful for generating inventory recommendations directly from the data stored in Redshift.
Option B: Use SQL to invoke a remote SageMaker endpoint for prediction.
You can use SQL in Redshift to call a SageMaker endpoint for real-time inference. By invoking a SageMaker endpoint from within Redshift, the company can get real-time predictions on inventory, allowing for integration between the data warehouse and the machine learning model hosted in SageMaker.
Option C (offline model training) and Option D (creating dashboards with SageMaker Autopilot) are not relevant to the real-time prediction and recommendation requirements.
Option E (archiving inventory reports in Redshift) is not related to making predictions or recommendations.
Reference:
Amazon Redshift ML Documentation
Invoking SageMaker Endpoints from SQL
問題 #206
A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?
答案：C
解題說明：
The best solution to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs is to use the query result reuse feature of Amazon Athena for the SQL queries. This feature allows you to run the same query multiple times without incurring additional charges, as long as the underlying data has not changed and the query results are still in the query result location in Amazon S31. This feature is useful for scenarios where you have a petabyte-scale dataset that is updated infrequently, such as once a day, and you have a BI application that runs the same queries repeatedly, such as every hour. By using the query result reuse feature, you can reduce the amount of data scanned by your queries and save on the cost of running Athena. You can enable or disable this feature at the workgroup level or at the individual query level1.
Option A is not the best solution, as configuring an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day would not cost optimize the company's use of Amazon Athena, but rather increase the cost and complexity. Amazon S3 Lifecycle policies are rules that you can define to automatically transition objects between different storage classes based on specified criteria, such as the age of the object2. S3 Glacier Deep Archive is the lowest-cost storage class in Amazon S3, designed for long-term data archiving that is accessed once or twice in a year3. While moving data to S3 Glacier Deep Archive can reduce the storage cost, it would also increase the retrieval cost and latency, as it takes up to 12 hours to restore the data from S3 Glacier Deep Archive3. Moreover, Athena does not support querying data that is in S3 Glacier or S3 Glacier Deep Archive storage classes4. Therefore, using this option would not meet the requirements of running on-demand SQL queries on the dataset.
Option C is not the best solution, as adding an Amazon ElastiCache cluster between the BI application and Athena would not cost optimize the company's use of Amazon Athena, but rather increase the cost and complexity. Amazon ElastiCache is a service that offers fully managed in-memory data stores, such as Redis and Memcached, that can improve the performance and scalability of web applications by caching frequently accessed data. While using ElastiCache can reduce the latency and load on the BI application, it would not reduce the amount of data scanned by Athena, which is the main factor that determines the cost of running Athena. Moreover, using ElastiCache would introduce additional infrastructure costs and operational overhead, as you would have to provision, manage, and scale the ElastiCache cluster, and integrate it with the BI application and Athena.
Option D is not the best solution, as changing the format of the files that are in the dataset to Apache Parquet would not cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs, but rather increase the complexity. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However, changing the format of the files that are in the dataset to Apache Parquet would require additional processing and transformation steps, such as using AWS Glue or Amazon EMR to convert the files from their original format to Parquet, and storing the converted files in a separate location in Amazon S3. This would increase the complexity and the operational overhead of the data pipeline, and also incur additional costs for using AWS Glue or Amazon EMR. Reference:
Query result reuse
Amazon S3 Lifecycle
S3 Glacier Deep Archive
Storage classes supported by Athena
[What is Amazon ElastiCache?]
[Amazon Athena pricing]
[Columnar Storage Formats]
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
問題 #207
A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.
A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.
The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?
答案：C
解題說明：
To achieve the most cost-effective storage solution, the data engineer needs to use an S3 Lifecycle policy that transitions objects to lower-cost storage classes based on their access patterns, and deletes them when they are no longer needed. The storage classes should also provide high availability, which means they should be resilient to the loss of data in a single Availability Zone1. Therefore, the solution must include the following steps:
Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is designed for data that is accessed less frequently, but requires rapid access when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost and a retrieval fee2. Therefore, it is suitablefor data files that are accessed once or twice each month. S3 Standard-IA also provides high availability, as it stores data redundantly across multiple Availability Zones1.
Transfer objects to S3 Glacier Deep Archive after 2 years. S3 Glacier Deep Archive is the lowest-cost storage class that offers secure and durable storage for data that is rarely accessed and can tolerate a
12-hour retrieval time. It is ideal for long-term archiving and digital preservation3. Therefore, it is suitable for data files that are accessed only once or twice each year. S3 Glacier Deep Archive also provides high availability, as it stores data across at least three geographically dispersed Availability Zones1.
Delete objects when they are no longer needed. The data engineer can specify an expiration action in the S3 Lifecycle policy to delete objects after a certain period of time. This will reduce the storage cost and comply with any data retention policies.
Option C is the only solution that includes all these steps. Therefore, option C is the correct answer.
Option A is incorrect because it transitions objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after
6 months. S3 One Zone-IA is similar to S3 Standard-IA, but it stores data in a single Availability Zone. This means it has a lower availability and durability than S3 Standard-IA, and it is not resilient to the loss of data in a single Availability Zone1. Therefore, it does not provide high availability as required.
Option B is incorrect because it transfers objects to S3 Glacier Flexible Retrieval after 2 years. S3 Glacier Flexible Retrieval is a storage class that offers secure and durable storage for data that is accessed infrequently and can tolerate a retrieval time of minutes to hours. It is more expensive than S3 Glacier Deep Archive, and it is not suitable for data that is accessed only once or twice each year3. Therefore, it is not the most cost-effective option.
Option D is incorrect because it combines the errors of option A and B. It transitions objects to S3 One Zone-IA after 6 months, which does not provide high availability, and it transfers objects to S3 Glacier Flexible Retrieval after 2 years, which is not the most cost-effective option.
References:
1: Amazon S3 storage classes - Amazon Simple Storage Service
2: Amazon S3 Standard-Infrequent Access (S3 Standard-IA) - Amazon Simple Storage Service
3: Amazon S3 Glacier and S3 Glacier Deep Archive - Amazon Simple Storage Service
[4]: Expiring objects - Amazon Simple Storage Service
[5]: Managing your storage lifecycle - Amazon Simple Storage Service
[6]: Examples of S3 Lifecycle configuration - Amazon Simple Storage Service
[7]: Amazon S3 Lifecycle further optimizes storage cost savings with new features - What's New with AWS
問題 #208
A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.
A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.
How should the company address the CloudWatch alarm?
答案：D
解題說明：
TheRootDiskUsedmetric for the MSK cluster indicates that the storage on the broker is reaching its capacity.
The best solution is to expand the storage of the MSK broker and enable automatic storage expansion to prevent future alarms.
* Expand MSK Broker Storage:
* AWS Managed Streaming for Apache Kafka (MSK) allows you toexpand the broker storageto accommodate growing data volumes. Additionally,auto-expansionof storage can be configured to ensure that storage grows automatically as the data increases.
Reference:Amazon MSK Cluster Storage Expansion
Alternatives Considered:
B (Expand Zookeeper storage): Zookeeper is responsible for managing Kafka metadata and not for storing data, so increasing Zookeeper storage won't resolve the root disk issue.
C (Update instance type): Changing the instance type would increase computational resources but not directly address the storage problem.
D (Target-Volume-in-GiB): This parameter is irrelevant for the existing topic and will not solve the storage issue.
References:
Amazon MSK Storage Auto Scaling
問題 #209
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