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Amazon AWS Certified Solutions Architect - Associate (SAA-C03) Exam Sample Questions (Q243-Q248):NEW QUESTION # 243
A company is developing a two-tier web application on AWS. The company's developers have deployed the application on an Amazon EC2 instance that connects directly to a backend Amazon RDS database. The company must not hardcode database credentials in the application. The company must also implement a solution to automatically rotate the database credentials on a regular basis.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Explanation
https://docs.aws.amazon.com/secretsmanager/latest/userguide/create_database_secret.html
NEW QUESTION # 244
A company has a multi-tier application deployed on several Amazon EC2 instances in an Auto Scaling group. An Amazon RDS for Oracle instance is the application's data layer that uses Oracle-specific PL/SQL functions. Traffic to the application has been steadily increasing. This is causing the EC2 instances to become overloaded and the RDS instance to run out of storage. The Auto Scaling group does not have any scaling metrics and defines the minimum healthy instance count only. The company predicts that traffic will continue to increase at a steady but unpredictable rate before levelling off.
What should a solutions architect do to ensure the system can automatically scale for the increased traffic? (Select TWO.)
Answer: A,D
NEW QUESTION # 245
A solutions architect is creating a new VPC design There are two public subnets for the load balancer, two private subnets for web servers and two private subnets for MySQL The web servers use only HTTPS The solutions architect has already created a security group tor the load balancer allowing port 443 from 0 0 0 0/0 Company policy requires that each resource has the teas! access required to still be able to perform its tasks Which additional configuration strategy should the solutions architect use to meet these requirements?
Answer: B
NEW QUESTION # 246
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?
Answer: C
Explanation:
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/
NEW QUESTION # 247
A company collects temperature, humidity, and atmospheric pressure data in cities across multiple continents.
The average volume of data collected per site each day is 500 GB. Each site has a high-speed internet connection. The company's weather forecasting applications are based in a single Region and analyze the data daily.
What is the FASTEST way to aggregate data from all of these global sites?
Answer: D
Explanation:
Explanation
You might want to use Transfer Acceleration on a bucket for various reasons, including the following:
You have customers that upload to a centralized bucket from all over the world.
You transfer gigabytes to terabytes of data on a regular basis across continents.
You are unable to utilize all of your available bandwidth over the Internet when uploading to Amazon S3.
https://docs.aws.amazon.com/AmazonS3/latest/dev/transfer-acceleration.html
https://aws.amazon.com/s3/transfer-acceleration/#:~:text=S3%20Transfer%20Acceleration%20(S3TA)%20reduc
"Amazon S3 Transfer Acceleration can speed up content transfers to and from Amazon S3 by as much as
50-500% for long-distance transfer of larger objects. Customers who have either web or mobile applications with widespread users or applications hosted far away from their S3 bucket can experience long and variable upload and download speeds over the Internet"
https://docs.aws.amazon.com/AmazonS3/latest/userguide/mpuoverview.html
"Improved throughput - You can upload parts in parallel to improve throughput."
NEW QUESTION # 248
......
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