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| Section | Weight | Objectives |
|---|---|---|
| Data Security and Governance | 18% | - Apply authentication and authorization
|
| Data Store Management | 26% | - Design data models
|
| Data Ingestion and Transformation | 34% | - Perform data ingestion
|
| Data Operations and Support | 22% | - Manage and troubleshoot data processes
|
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NEW QUESTION # 166
A company needs to implement a new inventory management system that provides near real-time updates and visibility across all AWS Regions. The new solution must provide centralized access control over data access and permissions. The company has a separate inventory management team assigned to each Region. Each inventory management team needs to update inventory levels.
A data engineer must implement Amazon Redshift data sharing with write capabilities. The solution must follow the principle of least privilege.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
Amazon Redshift data sharing now supports bi-directional and selective write access through datashare namespaces.
This allows centralized management of permissions from headquarters while giving Regional clusters controlled write access to specific datasets - adhering to least privilege and minimizing admin overhead.
"Use Redshift datashares with write permissions and namespaces to enable controlled cross-Region data collaboration while maintaining centralized governance."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This configuration ensures near real-time inventory updates, centralized access management, and compliance with AWS security best practices.
NEW QUESTION # 167
A company wants to migrate an application and an on-premises Apache Kafka server to AWS. The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy.
Which solution will meet these requirements with the LEAST management overhead?
Answer: C
Explanation:
Problem Analysis:
The company needs to migrate both an application and an on-premises Apache Kafka server to AWS.
Incremental updates from an on-premises Oracle database are processed by Kafka.
The solution must follow a replatform migration strategy, prioritizing minimal changes and low management overhead.
Key Considerations:
Replatform Strategy: This approach keeps the application and architecture as close to the original as possible, reducing the need for refactoring.
The solution must provide a managed Kafka service to minimize operational burden.
Low overhead solutions like serverless services are preferred.
Solution Analysis:
Option A: Kinesis Data Streams
Kinesis Data Streams is an AWS-native streaming service but is not a direct substitute for Kafka.
This option would require significant application refactoring, which does not align with the replatform strategy.
Option B: MSK Provisioned Cluster
Managed Kafka service with fully configurable clusters.
Provides the same Kafka APIs but requires cluster management (e.g., scaling, patching), increasing management overhead.
Option C: Amazon Kinesis Data Firehose
Kinesis Data Firehose is designed for data delivery rather than real-time streaming and processing.
Not suitable for Kafka-based applications.
Option D: MSK Serverless
MSK Serverless eliminates the need for cluster management while maintaining compatibility with Kafka APIs.
Automatically scales based on workload, reducing operational overhead.
Ideal for replatform migrations, as it requires minimal changes to the application.
Final Recommendation:
Amazon MSK Serverless is the best solution for migrating the Kafka server and application with minimal changes and the least management overhead.
Reference:
Amazon MSK Serverless Overview
Comparison of Amazon MSK and Kinesis
NEW QUESTION # 168
A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
Which solution will meet these requirements?
Answer: B
Explanation:
This solution meets the requirements of optimizing the performance of table SQL queries without increasing the size of the cluster. By using the ALL distribution style for rarely updated small tables, you can ensure that the entire table is copied to every node in the cluster, which eliminates the need for data redistribution during joins. This can improve query performance significantly, especially for frequently joined dimension tables. However, using the ALL distribution style also increases the storage space and the load time, so it is only suitable for small tables that are not updated frequently or extensively. By specifying primary and foreign keys for all tables, you can help the query optimizer to generate better query plans and avoid unnecessary scans or joins. You can also use the AUTO distribution style to let Amazon Redshift choose the optimal distribution style based on the table size and the query patterns. Reference:
Choose the best distribution style
Distribution styles
Working with data distribution styles
NEW QUESTION # 169
A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?
Answer: B
Explanation:
To create a new table named cities_usa in Amazon Athena based on a subset of data from the existing cities_world table, you should use anINSERT INTOstatement combined with aSELECTstatement to filter only the records where the country is 'usa'. The correct SQL syntax would be:
* Option A: INSERT INTO cities_usa (city, state) SELECT city, state FROM cities_world WHERE country='usa';This statement inserts only the cities and states where the country column has a value of
'usa' from the cities_world table into the cities_usa table. This is a correct approach to create a new table with data filtered from an existing table in Athena.
Options B, C, and Dare incorrect due to syntax errors or incorrect SQL usage (e.g., the MOVE command or the use of UPDATE in a non-relevant context).
References:
Amazon Athena SQL Reference
Creating Tables in Athena
NEW QUESTION # 170
A retail company stores order information in an Amazon Aurora table named Orders. The company needs to create operational reports from the Orders table with minimal latency. The Orders table contains billions of rows, and over 100,000 transactions can occur each second.
A marketing team needs to join the Orders data with an Amazon Redshift table named Campaigns in the marketing team's data warehouse. The operational Aurora database must not be affected.
Which solution will meet these requirements with the LEAST operational effort?
Answer: B
NEW QUESTION # 171
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