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| Section | Weight | Objectives |
|---|---|---|
| Design High-Performing Architectures | 24% | - Determine high-performing database solutions
|
| Design Resilient Architectures | 26% | - Design highly available and/or fault-tolerant architectures
|
| Design Cost-Optimized Architectures | 20% | - Design cost-optimized network architectures
|
| Design Secure Architectures | 30% | - Design secure access to AWS resources
|
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問題 #458
An application is loading hundreds of JSON documents into an Amazon S3 bucket every hour which is registered in AWS Lake Formation as a data catalog. The Data Analytics team uses Amazon Athena to run analyses on these data, but due to the volume, most queries take a long time to complete.
What change should be made to improve the query performance while ensuring data security?
答案：D
解題說明：
Amazon Athena supports a wide variety of data formats like CSV, TSV, JSON, or Textfiles and also supports open-source columnar formats such as Apache ORC and Apache Parquet. Athena also supports compressed data in Snappy, Zlib, LZO, and GZIP formats. By compressing, partitioning, and using columnar formats you can improve performance and reduce your costs.
Parquet and ORC file formats both support predicate pushdown (also called predicate filtering). Parquet and ORC both have blocks of data that represent column values. Each block holds statistics for the block, such as max/min values. When a query is being executed, these statistics determine whether the block should be read or skipped.
Athena charges you by the amount of data scanned per query. You can save on costs and get better performance if you partition the data, compress data, or convert it to columnar formats such as Apache Parquet.
Apache Parquet is an open-source columnar storage format that is 2x faster to unload and takes up 6x less storage in Amazon S3 as compared to other text formats. One can COPY Apache Parquet and Apache ORC file formats from Amazon S3 to your Amazon Redshift cluster. Using AWS Glue, one can configure and run a job to transform CSV data to Parquet. Parquet is a columnar format that is well suited for AWS analytics services like Amazon Athena and Amazon Redshift Spectrum.
When an integrated AWS service requests access to data in an Amazon S3 location that is access- controlled by AWS Lake Formation, Lake Formation supplies temporary credentials to access the data.
To enable Lake Formation to control access to underlying data at an Amazon S3 location, you register that location with Lake Formation.
To enable Lake Formation principals to read and write underlying data with access controlled by Lake Formation permissions:
- The Amazon S3 locations that contain the data must be registered with Lake Formation.
- Principals who create Data Catalog tables that point to underlying data locations must have data location permissions.
- Principals who read and write underlying data must have Lake Formation data access permissions on the Data Catalog tables that point to the underlying data locations.
- Principals who read and write underlying data must have the lakeformation:GetDataAccess IAM permission.
Thus, the correct answer is: Transform the JSON data into Apache Parquet format. Ensure that the user has an lakeformation:GetDataAccess IAM permission for underlying data access control.
The option that says: Convert the JSON documents into CSV format. Provide fine-grained named resource access control to specific databases or tables in AWS Lake Formation is incorrect because Athena queries performed against row-based formats like CSV are slower than columnar file formats like Apache Parquet.
The option that says:Apply minification on the data and implement the Lake Formation tag-based access control (LF-TBAC) authorization strategy using IAM Tags to ensure security is incorrect. Although minifying the JSON file might reduce its overall file size, there won't be a significant difference in terms of querying performance. LF-TBAC is a type of an attribute-based access control (ABAC) that defines permissions based on certain attributes, such as tags in AWS. LF-TBAC uses LF-Tags to grant Lake Formation permissions and not regular IAM Tags.
The option that says: Compress the data into GZIP format before storing in the S3 bucket. Apply an IAM policy with aws:SourceArn and aws:SourceAccount global condition context keys in Lake Formation that prevents cross-service confused deputy problems and other security issues. is incorrect. Compressing the files prior to storing them in Amazon S3 will only save storage costs. As for query performance, it won't have much improvement. In addition, using an IAM Policy to prevent cross-service confused deputy issues is not warranted in this scenario. Having an lakeformation:GetDataAccess IAM permission for underlying data access control should suffice.
References: https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/
https://docs.aws.amazon.com/lake-formation/latest/dg/access-control-underlying-data.html
https://docs.aws.amazon.com/lake-formation/latest/dg/TBAC-overview.html Check out this Amazon Athena Cheat Sheet: https://tutorialsdojo.com/amazon-athena/
問題 #459
An ecommerce company wants to collect user clickstream data from the company's website for real-time analysis. The website experiences fluctuating traffic patterns throughout the day. The company needs a scalable solution that can adapt to varying levels of traffic.
Which solution will meet these requirements?
答案：C
解題說明：
Amazon Kinesis Data Streams in on-demand mode is purpose-built for real-time ingestion of streaming data and scales automatically with traffic, which is ideal for fluctuating workloads like clickstream data.
Combined with AWS Lambda, which scales concurrently based on incoming events, this setup ensures efficient, real-time processing with minimal configuration and cost overhead.
Option B involves Firehose, which is not optimal for low-latency processing. Option C is incorrect as Kinesis Video Streams is designed for video data, not clickstream. Option D introduces Flink, which adds unnecessary complexity when Lambda suffices.
問題 #460
A company recently migrated a data warehouse to AWS. The company has an AWS Direct Connect connection to AWS. Company users query the data warehouse by using a visualization tool. The average size of the queries that the data warehouse returns is 50 MB. The average visualization that the visualization tool produces is 500 KB in size. The result sets that the data warehouse returns are not cached. The company wants to optimize costs for data transfers between the data warehouse and the company. Which solution will meet this requirement?
答案：D
解題說明：
A). On-premises tool via internet: Incurs high costs due to large data transfers over the internet.
B). AWS Region tool via internet: Does not utilize Direct Connect, leading to potential latency and higher costs.
C). On-premises tool via Direct Connect: Adds latency for querying and visualization.
D). AWS Region tool via Direct Connect: Reduces latency and leverages Direct Connect for optimized data transfer costs.
問題 #461
A company runs its critical storage application in the AWS Cloud. The application uses Amazon S3 in two AWS Regions. The company wants the application to send remote user data to the nearest S3 bucket with no public network congestion. The company also wants the application to fail over with the least amount of management of Amazon S3.
Which solution will meet these requirements?
答案：B
解題說明：
To meet the requirement of low-latency global access and failover with minimal management, the best solution is to use Amazon S3 Multi-Region Access Points (MRAP) with Cross-Region Replication (CRR).
Multi-Region Access Points provide a global endpoint that automatically routes requests to the nearest AWS Region using the AWS Global Accelerator infrastructure. This avoids public internet congestion and ensures low-latency access.
When combined with S3 Cross-Region Replication, data is automatically synchronized between buckets in different Regions, enabling active-active setup.
In case of a Regional failure, S3 Multi-Region Access Points handle failover automatically, requiring no manual intervention.
Options A and C require manual management and configuration of endpoints per Region. Option B misrepresents MRAP-it is used for active-active, not active-passive.
Reference:
S3 Multi-Region Access Points
S3 Cross-Region Replication
問題 #462
A company has an application that ingests incoming messages. These messages are then quickly consumed by dozens of other applications and microservices.
The number of messages varies drastically and sometimes spikes as high as 100,000 each second. The company wants to decouple the solution and increase scalability.
Which solution meets these requirements?
答案：A
解題說明：
Explanation
https://aws.amazon.com/sqs/features/
問題 #463
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