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NEW QUESTION # 48
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: B
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 # 49
[Design Secure Architectures]
A security team wants to limit access to specific services or actions in all of the team's AWS accounts. All accounts belong to a large organization in AWS Organizations. The solution must be scalable and there must be a single point where permissions can be maintained.
What should a solutions architect do to accomplish this?
Answer: C
Explanation:
Service control policies (SCPs) are one type of policy that you can use to manage your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization, allowing you to ensure your accounts stay within your organization's access control guidelines. Seehttps://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scp.html.
NEW QUESTION # 50
A company runs a Microsoft Windows SMB file share on-premises to support an application. The company wants to migrate the application to AWS. The company wants to share storage across multiple Amazon EC2 instances.
Which solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)
Answer: A,B
Explanation:
* A. Amazon EFS: Provides a scalable, shared file storage solution with minimal operational overhead. It's ideal for Linux-based workloads.
* B. Amazon FSx for NetApp ONTAP: More suited for workloads requiring NetApp-specific features.
* C. Amazon EBS: Requires manual management of file shares, which increases operational overhead.
* D. Amazon FSx for Windows File Server: Best suited for Windows SMB workloads with low operational overhead.
* E. Amazon FSx for OpenZFS: Better for Linux and Unix-based workloads.
References: Amazon EFS, Amazon FSx
NEW QUESTION # 51
A company has an application that runs on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster on Amazon EC2 instances. The application has a U1 that uses Amazon DynamoDB and data services that use Amazon S3 as part of the application deployment.
The company must ensure that the EKS Pods for the U1 can access only Amazon DynamoDB and that the EKS Pods for the data services can access only Amazon S3. The company uses AWS Identity and Access Management |IAM).
Which solution meets these requirements?
Answer: C
NEW QUESTION # 52
[Design High-Performing Architectures]
A company has implemented a self-managed DNS service on AWS. The solution consists of the following:
* Amazon EC2 instances in different AWS Regions
* Endpomts of a standard accelerator m AWS Global Accelerator
The company wants to protect the solution against DDoS attacks What should a solutions architect do to meet this requirement?
Answer: D
Explanation:
AWS Shield is a managed service that provides protection against Distributed Denial of Service (DDoS) attacks for applications running on AWS. AWS Shield Standard is automatically enabled to all AWS customers at no additional cost. AWS Shield Advanced is an optional paid service. AWS Shield Advanced provides additional protections against more sophisticated and larger attacks for your applications running on Amazon Elastic Compute Cloud (EC2), Elastic Load Balancing (ELB), Amazon CloudFront, AWS Global Accelerator, and Route 53.
https://docs.aws.amazon.com/waf/latest/developerguide/ddos-event-mitigation-logic-gax.html
NEW QUESTION # 53
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