Amazon SAA-C03 PDF, SAA-C03 Latest Exam Question
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Amazon SAA-C03 (Amazon AWS Certified Solutions Architect - Associate) Certification Exam is a globally recognized certification exam designed for professionals seeking to validate their skills and expertise in designing and deploying secure, scalable, and reliable applications on the Amazon Web Services (AWS) platform. SAA-C03 Exam is intended for individuals who are proficient in AWS technologies, understand AWS architecture best practices, and have practical experience in designing and deploying scalable, fault-tolerant, and highly available systems.
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Amazon AWS Certified Solutions Architect - Associate Sample Questions (Q744-Q749):
NEW QUESTION # 744
A company collects 10 GB of telemetry data every day from multiple devices. The company stores the data in an Amazon S3 bucket that is in a source data account.
The company has hired several consulting agencies to analyze the company's data. Each agency has a unique AWS account. Each agency requires read access to the company's data.
The company needs a secure solution to share the data from the source data account to the consulting agencies.
Which solution will meet these requirements with the LEAST operational effort?
- A. Set up an Amazon CloudFront distribution. Use the S3 bucket as the origin.
- B. Set up an IAM user for each agency in the source data account. Grant each agency IAM user access to the company's S3 bucket.
- C. Configure cross-account access for the S3 bucket to the accounts that the agencies own.
- D. Make the S3 bucket public for a limited time. Inform only the agencies that the bucket is publicly accessible.
Answer: C
Explanation:
The most secure and least operationally intensive method is to configure cross-account access using resource-based policies on the S3 bucket. This allows trusted external AWS accounts (consulting agencies) to securely access the S3 data without the need to manage user credentials or build additional infrastructure.
Options A and B pose security risks. Option D increases operational complexity and violates least privilege by managing external users inside your AWS account.
NEW QUESTION # 745
A company is developing a highly available natural language processing NLP application. The application handles large volumes of concurrent requests. The application performs NLP tasks such as entity recognition, sentiment analysis, and key phrase extraction on text data.
The company needs to store data that the application processes in a highly available and scalable database.
Which solution will meet these requirements?
- A. Create an Amazon API Gateway REST API endpoint to handle incoming requests. Configure the REST API to invoke an AWS Lambda function for each request. Configure the Lambda function to call Amazon Comprehend to perform NLP tasks on the text data. Store the processed data in Amazon DynamoDB.
- B. Create an Amazon SQS queue to buffer incoming requests. Deploy the NLP application on Amazon EC2 instances in an Auto Scaling group. Use Amazon Comprehend to perform NLP tasks. Store the processed data in an Amazon RDS database.
- C. Create an Amazon API Gateway WebSocket API endpoint to handle incoming requests. Configure the WebSocket API to invoke an AWS Lambda function for each request. Configure the Lambda function to call Amazon Textract to perform NLP tasks on the text data. Store the processed data in Amazon ElastiCache.
- D. Create an Amazon API Gateway HTTP API endpoint to handle incoming requests. Configure the HTTP API to invoke an AWS Lambda function for each request. Configure the Lambda function to call Amazon Translate to perform NLP tasks on the text data. Store the processed data in Amazon ElastiCache.
Answer: A
Explanation:
The right solution isAPI Gateway + Lambda + Amazon Comprehend + DynamoDB. AWS documents that Amazon Comprehend is the NLP service for detecting entities, sentiment, and key phrases, which matches the workload exactly. DynamoDB is a fully managed, distributed database designed for high-throughput workloads and single-digit millisecond performance at scale, making it a strong fit for a highly available application handling many concurrent requests. The other answers misuse services: Amazon Translate is for translation, Amazon Textract is for extracting text from documents, and ElastiCache is a cache rather than the durable primary database described in the question. This makes option A the only architecture that aligns with both the NLP functions and the scalable database requirement. (AWS Documentation)
NEW QUESTION # 746
A company uses AWS CloudFormation to deploy IAM resources within accounts that AWS Control Tower governs. The security team wants to prevent the deployment of IAM roles that include inline policies with the following statements:
"Effect": "Allow", "Action": "*", "Resource": "*"
Which solution will meet this requirement?
- A. Use AWS Control Tower proactive controls to block CloudFormation stacks that match these inline policy statements.
- B. Use AWS Config to create a rule that detects these statements in any inline IAM policies. Configure the rule to automatically remove these statements by using the AWS-DeleteIAMInlinePolicy remediation.
- C. Use AWS Control Tower detective controls to detect and delete IAM inline policies that contain these statements upon deployment.
- D. Use AWS Config to create a rule that detects these statements in inline IAM policies and sends a notification to the security team.
Answer: A
Explanation:
Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):
The security requirement is preventive: "prevent the deployment" of IAM roles that contain an overly permissive inline policy (Allow on Action:* and Resource:*). Preventive enforcement should occur at deployment time so noncompliant IAM resources are blocked before they exist. In a Control Tower-governed environment where resources are provisioned through CloudFormation, the most appropriate mechanism is a proactive control that evaluates templates and denies noncompliant stack operations.
Option A matches this intent. Control Tower proactive controls are designed to provide preventative guardrails by checking resource configurations (including Infrastructure as Code deployments) against defined rules and blocking deployments that violate policy. This stops risky IAM roles from being created in the first place, which is preferable to after-the-fact detection and cleanup.
Options B, C, and D are detective or reactive patterns. Detective controls (and AWS Config rules) can identify noncompliance after resources are created, but that does not "prevent deployment." Even if remediation is automated (Option C), there is still a window where an overly permissive role exists and could be assumed or exploited. Option D only notifies, which is insufficient. Option B suggests deleting policies on deployment, but Control Tower detective controls are not intended to automatically delete deployed IAM policies; they primarily detect and report drift/noncompliance.
Therefore, A is the best solution because it enforces the requirement proactively by blocking noncompliant CloudFormation deployments, maintaining a stronger security posture and reducing operational burden associated with detection and remediation.
NEW QUESTION # 747
A company has primary and secondary data centers that are 500 miles (804.7 km) apart and interconnected with high-speed fiber-optic cable. The company needs a highly available and secure network connection between its data centers and a VPC on AWS for a mission-critical workload.
A solutions architect must choose a connection solution that provides maximum resiliency.
Which solution meets these requirements?
- A. Two AWS Direct Connect connections from each of the primary and secondary data centers terminating at two Direct Connect locations on two separate devices
- B. Two AWS Direct Connect connections from the primary data center terminating at two Direct Connect locations on two separate devices
- C. A single AWS Direct Connect connection from each of the primary and secondary data centers terminating at one Direct Connect location on the same device
- D. A single AWS Direct Connect connection from each of the primary and secondary data centers terminating at one Direct Connect location on two separate devices
Answer: A
Explanation:
For maximum resiliency and fault tolerance in a mission-critical scenario, AWS recommends redundant Direct Connect connections from multiple data centers to multiple AWS Direct Connect locations.
This protects against:
Data center failure
Device failure
Location outages
"For workloads that require high availability, we recommend that you use multiple Direct Connect connections at multiple Direct Connect locations."
- AWS Direct Connect Resiliency Recommendations
Option C follows AWS maximum resiliency model.
Reference:
AWS Direct Connect Resiliency Models
High Availability Using AWS Direct Connect
NEW QUESTION # 748
A company runs a three-tier web application in a VPC across multiple Availability Zones. Amazon EC2 instances run in an Auto Scaling group for the application tier.
The company needs to make an automated scaling plan that will analyze each resource's daily and weekly historical workload trends. The configuration must scale resources appropriately according to both the forecast and live changes in utilization.
Which scaling strategy should a solutions architect recommend to meet these requirements?
- A. Implement dynamic scaling with step scaling based on average CPU utilization from the EC2 instances.
- B. Create an automated scheduled scaling action based on the traffic patterns of the web application.
- C. Enable predictive scaling to forecast and scale. Configure dynamic scaling with target tracking.
- D. Set up a simple scaling policy. Increase the cooldown period based on the EC2 instance startup time
Answer: C
Explanation:
This solution meets the requirements because it allows the company to use both predictive scaling and dynamic scaling to optimize the capacity of its Auto Scaling group. Predictive scaling uses machine learning to analyze historical data and forecast future traffic patterns. It then adjusts the desired capacity of the group in advance of the predicted changes. Dynamic scaling uses target tracking to maintain a specified metric (such as CPU utilization) at a target value. It scales the group in or out as needed to keep the metric close to the target. By using both scaling methods, the company can benefit from faster, simpler, and more accurate scaling that responds to both forecasted and live changes in utilization.
Reference:
Predictive scaling for Amazon EC2 Auto Scaling
[Target tracking scaling policies for Amazon EC2 Auto Scaling]
NEW QUESTION # 749
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