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| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Data Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Data Operations and Support | 22% | - Monitor and maintain data pipelines - Troubleshoot data workflow issues |
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167. Frage
A company needs to load customer data that comes from a third party into an Amazon Redshift data warehouse. The company stores order data and product data in the same data warehouse. The company wants to use the combined dataset to identify potential new customers.
A data engineer notices that one of the fields in the source data includes values that are in JSON format.
How should the data engineer load the JSON data into the data warehouse with the LEAST effort?
Antwort: C
Begründung:
In Amazon Redshift, the SUPER data type is designed specifically to handle semi-structured data like JSON, Parquet, ORC, and others. By using the SUPER data type, Redshift can ingest and query JSON data without requiring complex data flattening processes, thus reducing the amount of preprocessing required before loading the data. The SUPER data type also works seamlessly with Redshift Spectrum, enabling complex queries that can combine both structured and semi-structured datasets, which aligns with the company's need to use combined datasets to identify potential new customers.
Using the SUPER data type also allows for automatic parsing and query processing of nested data structures through Amazon Redshift's PARTITION BY and JSONPATH expressions, which makes this option the most efficient approach with the least effort involved. This reduces the overhead associated with using tools like AWS Glue or Lambda for data transformation.
References:
* Amazon Redshift Documentation - SUPER Data Type
* AWS Certified Data Engineer - Associate Training: Building Batch Data Analytics Solutions on AWS
* AWS Certified Data Engineer - Associate Study Guide
By directly leveraging the capabilities of Redshift with the SUPER data type, the data engineer ensures streamlined JSON ingestion with minimal effort while maintaining query efficiency.
168. Frage
A company stores employee data in Amazon Redshift A table named Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key. Which queries will MOST increase the speed of a query by using a compound sort key of the table? (Select TWO.)
Antwort: B,C
Begründung:
In Amazon Redshift, a compound sort key is designed to optimize the performance of queries that use filtering and join conditions on the columns in the sort key. A compound sort key orders the data based on the first column, followed by the second, and so on. In the scenario given, the compound sort key consists of Region ID, Department ID, and Role ID. Therefore, queries that filter on the leading columns of the sort key are more likely to benefit from this order.
* Option B: "Select * from Employee where Region ID='North America' and Department ID=20;" This query will perform well because it uses both the Region ID and Department ID, which are the first two columns of the compound sort key. The order of the columns in the WHERE clause matches the order in the sort key, thus allowing the query to scan fewer rows and improve performance.
* Option C: "Select * from Employee where Department ID=20 and Region ID='North America';" This query also benefits from the compound sort key because it includes both Region ID and Department ID, which are the first two columns in the sort key. Although the order in the WHERE clause does not match exactly, Amazon Redshift will still leverage the sort key to reduce the amount of data scanned, improving query speed.
* Options A, D, and E are less optimal because they do not utilize the sort key as effectively:
* Option A only filters by the Region ID, which may still use the sort key but does not take full advantage of the compound nature.
* Option D uses only Role ID, the last column in the compound sort key, which will not benefit much from sorting since it is the third key in the sort order.
* Option E filters on Region ID and Role ID but skips the Department ID column, making it less efficient for the compound sort key.
References:
* Amazon Redshift Documentation - Sorting Data
* AWS Certified Data Analytics Study Guide
* AWS Certification - Data Engineer Associate Exam Guide
169. Frage
A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools.
Which solution will meet these requirements with the LEAST operational overhead?
Antwort: C
Begründung:
This solution meets the requirements of implementing real-time analytics capabilities with the least operational overhead. By creating an external schema in Amazon Redshift, you can access the data from Kinesis Data Streams using SQL queries without having to load the data into the cluster. By creating a materialized view on top of the stream, you can store the results of the query in the cluster and make them available for analysis. By setting the materialized view to auto refresh, you can ensure that the view is updated with the latest data from the stream at regular intervals. This way, you can derive near real-time insights by using existing BI and analytics tools. References:
Amazon Redshift streaming ingestion
Creating an external schema for Amazon Kinesis Data Streams
Creating a materialized view for Amazon Kinesis Data Streams
170. Frage
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?
Antwort: C
Begründung:
AWS Data Exchange is a service that makes it easy to find, subscribe to, and use third-party data in the cloud.
It provides a secure and reliable way to access and integrate data from various sources, such as data providers, public datasets, or AWS services. Using AWS Data Exchange, you can browse and subscribe to data products that suit your needs, and then use API calls or the AWS Management Console to export the data to Amazon S3, where you can use it with your existing analytics platform. This solution minimizes the effort and time required to incorporate third-party datasets, as you do not need to set up and manage data pipelines, storage, or access controls. You also benefit from the data quality and freshness provided by the data providers, who can update their data products as frequently as needed12.
The other options are not optimal for the following reasons:
* B. Use API calls to access and integrate third-party datasets from AWS. This option is vague and does not specify which AWS service or feature is used to access and integrate third-party datasets. AWS offers a variety of services and features that can help with data ingestion, processing, and analysis, but not all of them are suitable for the given scenario. For example, AWS Glue is a serverless data integration service that can help you discover, prepare, and combine data from various sources, but it requires you to create and run data extraction, transformation, and loading (ETL) jobs, which can add operational overhead3.
* C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. This option is not feasible, as AWS CodeCommit is a source control service that hosts secure Git-based repositories, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is a service that enables you to capture, process, and analyze data streams in real time, such as clickstream data, application logs, or IoT telemetry. It does not support accessing and integrating data from AWS CodeCommit repositories, which are meant for storing and managing code, not data .
* D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). This option is also not feasible, as Amazon ECR is a fully managed container registry service that stores, manages, and deploys container images, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams does not support accessing and integrating data from Amazon ECR, which is meant for storing and managing container images, not data .
References:
* 1: AWS Data Exchange User Guide
* 2: AWS Data Exchange FAQs
* 3: AWS Glue Developer Guide
* : AWS CodeCommit User Guide
* : Amazon Kinesis Data Streams Developer Guide
* : Amazon Elastic Container Registry User Guide
* : Build a Continuous Delivery Pipeline for Your Container Images with Amazon ECR as Source
171. Frage
A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. After a merger, a query joining two large sales tables becomes slow. Table S1 has 10 billion records, Table S2 has 900 million records.
The query performance must improve.
Antwort: B,E
Begründung:
To optimize joins between large tables, Redshift recommends using KEY distribution on a common, high- cardinality join key to ensure co-location of data blocks. The Amazon Redshift Advisor identifies performance bottlenecks and provides recommendations for distribution and sort keys.
"Use KEY distribution when joining large tables on a common high-cardinality column. Use Redshift Advisor to identify query optimization opportunities."
172. Frage
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