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Amazon AWS Certified Solutions Architect - Associate SAA-C03 Prüfungsfragen mit Lösungen (Q496-Q501):

496. Frage
A company sells datasets to customers who do research in artificial intelligence and machine learning (Al
/ML) The datasets are large, formatted files that are stored in an Amazon S3 bucket in the us-east-1 Region The company hosts a web application that the customers use to purchase access to a given dataset The web application is deployed on multiple Amazon EC2 instances behind an Application Load Balancer After a purchase is made customers receive an S3 signed URL that allows access to the files.
The customers are distributed across North America and Europe The company wants to reduce the cost that is associated with data transfers and wants to maintain or improve performance.
What should a solutions architect do to meet these requirements?

A. Configure S3 Transfer Acceleration on the existing S3 bucket Direct customer requests to the S3 Transfer Acceleration endpoint Continue to use S3 signed URLs for access control
B. Deploy an Amazon CloudFront distribution with the existing S3 bucket as the origin Direct customer requests to the CloudFront URL Switch to CloudFront signed URLs for access control
C. Set up a second S3 bucket in the eu-central-1 Region with S3 Cross-Region Replication between the buckets Direct customer requests to the closest Region Continue to use S3 signed URLs for access control
D. Modify the web application to enable streaming of the datasets to end users. Configure the web application to read the data from the existing S3 bucket Implement access control directly in the application

Antwort: B

Begründung:
https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/PrivateContent.html

497. Frage
A company has a large fleet of vehicles that are equipped with internet connectivity to send telemetry to the company. The company receives over 1 million data points every 5 minutes from the vehicles. The company uses the data in machine learning (ML) applications to predict vehicle maintenance needs and to preorder parts. The company produces visual reports based on the captured data. The company wants to migrate the telemetry ingestion, processing, and visualization workloads to AWS. Which solution will meet these requirements?

A. Use Amazon Timestream for LiveAnalytics to store the data points. Grant Amazon SageMaker permission to access the data for processing. Use Amazon QuickSight to visualize the data.
B. Use Amazon Neptune to store the data points. Use Amazon Kinesis Data Streams to ingest data from Neptune into an AWS Lambda function for processing. Use Amazon QuickSight to visualize the data.
C. Use Amazon Timestream to for LiveAnalytics to store the data points. Grant Amazon SageMaker permission to access the data for processing. Use Amazon Athena to visualize the data.
D. Use Amazon DynamoDB to store the data points. Use DynamoDB Connector to ingest data from DynamoDB into Amazon EMR for processing. Use Amazon QuickSight to visualize the data.

Antwort: A

Begründung:
Amazon Timestream: Purpose-built time series database optimized for telemetry and IoT data ingestion and analytics.
* Amazon SageMaker: Provides ML capabilities for predictive maintenance workflows.
* Amazon QuickSight: Efficiently generates interactive, real-time visual reports from Timestream data.
* Optimized for Scale: Timestream efficiently handles large-scale telemetry data with time-series indexing and queries.
* Amazon Timestream Documentation

498. Frage
A company is developing a latency-sensitive application. Part of the application includes several AWS Lambda functions that need to initialize as quickly as possible. The Lambda functions are written in Java and contain initialization code outside the handlers to load libraries, initialize classes, and generate unique IDs. Which solution will meet the startup performance requirement MOST cost-effectively?

A. Update the Lambda functions to add a pre-snapshot hook. Move the code that generates unique IDs into the handlers. Publish a version of each Lambda function. Activate Lambda SnapStart for the published versions of the Lambda functions.
B. Publish a version of each Lambda function. Create an alias for each Lambda function. Configure each alias to point to its corresponding version. Set up provisioned concurrency configuration for each Lambda function to point to the corresponding alias.
C. Move all the initialization code to the handlers for each Lambda function. Activate Lambda SnapStart for each Lambda function. Configure SnapStart to reference the $LATEST version of each Lambda function.
D. Publish a version of each Lambda function. Set up a provisioned concurrency configuration for each Lambda function to point to the corresponding version. Activate Lambda SnapStart for the published versions of the Lambda functions.

Antwort: A

Begründung:
Same scenario as above-cost-effective cold start reduction for Java Lambdas is best achieved by using Lambda SnapStart with the correct code structure. Unique IDs and similar non-deterministic logic should be in the handler or in a pre-snapshot hook. SnapStart is supported only for published versions. This combination offers cold start performance at a lower cost than provisioned concurrency.

499. Frage
A company has deployed resources in the us-east-1 Region. The company also uses thousands of AWS Outposts servers deployed at remote locations around the world. These Outposts servers regularly download new software versions from us-east-1 that consist of hundreds of files. The company wants to improve the latency of the software download process.
Which solution will meet these requirements?

A. Create an Amazon S3 bucket in us-east-1. Set up a CloudFront distribution using all edge locations with caching enabled. Configure the bucket as the origin. Download the software by using signed URLs.
B. Create an Amazon S3 bucket in us-east-1. Configure S3 Transfer Acceleration. Configure the Outposts servers to download by using the acceleration endpoint.
C. Create an Amazon S3 bucket in us-east-1. Configure the bucket for static website hosting. Use bucket policies and ACLs to provide read access to the Outposts servers.
D. Create an Amazon S3 bucket in us-east-1 and a second bucket in us-west-2. Configure replication. Set up a CloudFront distribution with origin failover between the buckets. Download by using signed URLs.

Antwort: A

Begründung:
Amazon CloudFront uses a globally distributed network of edge locations that cache content close to users.
When Outposts servers around the world download large software packages, CloudFront provides significantly reduced latency due to edge caching. This is the AWS-recommended solution for accelerating downloads of static files at scale and across global locations.
S3 Transfer Acceleration optimizes uploads and downloads to a single Region but does not provide edge caching. Multi-Region replication with failover does not reduce latency globally because requests still must reach the regional origins. Therefore, CloudFront with caching enabled is the correct design for improving download speed worldwide.

500. Frage
A company wants to use a data lake that is hosted on Amazon S3 to provide analytics services for historical data. The data lake consists of 800 tables but is expected to grow to thousands of tables. More than 50 departments use the tables, and each department has hundreds of users.
Different departments need access to specific tables and columns. Which solution will meet these requirements with the LEAST operational overhead?

A. Create an IAM role for each department. Use AWS Lake Formation tag-based access control to grant each IAM role access to only the relevant resources. Create LF-tags that are attached to tables and columns. Use Amazon Athena to analyze the data.
B. Create an Amazon EMR cluster for each department. Configure an IAM service role for each EMR cluster to access relevant S3 files. For each department's users, create an IAM role that provides access to the relevant EMR cluster. Use Amazon EMR to analyze the data.
C. Create an Amazon Redshift cluster for each department. Use AWS Glue to ingest into the Redshift cluster only the tables and columns that are relevant to that department. Create Redshift database users. Grant the users access to the relevant department's Redshift cluster. Use Amazon Redshift to analyze the data.
D. Create an IAM role for each department. Use AWS Lake Formation based access control to grant each IAM role access to specific tables and columns. Use Amazon Athena to analyze the data.

Antwort: A

Begründung:
The requirement is to provide granular, scalable access to thousands of tables and columns in a data lake across many users and departments, with the least operational overhead.
AWS Lake Formation supports tag-based access control (TBAC) using LF-tags (Lake Formation tags), which allows you to assign tags to tables, columns, and databases. You can then define permissions on resources by specifying tags rather than managing permissions for individual resources. This approach is highly scalable and efficient when dealing with a growing number of tables and columns. By associating IAM roles to departments and granting access based on LF- tags, you dramatically reduce the operational burden as new tables or columns are added; you only need to assign the appropriate tags.
Amazon Athena can directly query data in S3 with Lake Formation providing fine-grained access control.

501. Frage
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