Free AWS Certified CloudOps Engineer - Associate SOA-C03 Exam braindumps download (AWS Certified CloudOps Engineer - Associate SOA-C03 exam dumps Free)

QUESTION: 36

A company runs an application on Amazon EC2 that connects to an Amazon Aurora PostgreSQL database. A developer accidentally drops a table from the database, causing application errors. Two hours later, a CloudOps engineer needs to recover the data and make the application functional again.

Which solution will meet this requirement?

Use the Aurora Backtrack feature to rewind the database to a specified time, 2 hours in the past.
Perform a point-in-time recovery on the existing database to restore the database to a specified point in time, 2 hours in the past.
Perform a point-in-time recovery and create a new database to restore the database to a specified point in time, 2 hours in the past. Reconfigure the application to use a new database endpoint.
Create a new Aurora cluster. Choose the Restore data from S3 bucket option. Choose log files up to the failure time 2 hours in the past.

Answer(s): C

Explanation:

In the AWS Cloud Operations and Aurora documentation, when data loss occurs due to human error such as dropped tables, Point-in-Time Recovery (PITR) is the recommended method for restoration. PITR creates a new Aurora cluster restored to a specific time before the failure.

The restored cluster has a new endpoint that must be reconfigured in the application to resume normal operations. AWS does not support performing PITR directly on an existing production database because that would overwrite current data.

Aurora Backtrack (Option A) applies only to Aurora MySQL, not PostgreSQL. Option B is incorrect because PITR cannot be executed in place. Option D refers to an import process from S3, which is unrelated to time-based recovery.

Hence, Option C is correct and follows the AWS CloudOps standard recovery pattern for PostgreSQL workloads.

Reference:

AWS Cloud Operations & Aurora Guide Section: Performing Point-in-Time Recovery for Aurora PostgreSQL Clusters

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QUESTION: 37

A company is using an Amazon Aurora MySQL DB cluster that has point-in-time recovery, backtracking, and automatic backup enabled. A CloudOps engineer needs to roll back the DB cluster to a specific recovery point within the previous 72 hours. Restores must be completed in the same production DB cluster.

Which solution will meet these requirements?

Create an Aurora Replica. Promote the replica to replace the primary DB instance.
Create an AWS Lambda function to restore an automatic backup to the existing DB cluster.
Use backtracking to rewind the existing DB cluster to the desired recovery point.
Use point-in-time recovery to restore the existing DB cluster to the desired recovery point.

Answer(s): C

Explanation:

As documented in AWS Cloud Operations and Database Recovery, Aurora Backtrack allows you to rewind the existing database cluster to a chosen point in time without creating a new cluster. This feature supports fine-grained rollback for accidental data changes, making it ideal for scenarios like table deletions or logical corruption.

Backtracking maintains continuous transaction logs and permits rewinding within a configurable window (up to 72 hours). It does not require creating a new cluster or endpoint, and it preserves the same production environment, fulfilling the operational requirement for in-place recovery.

In contrast, Point-in-Time Recovery (Option D) always creates a new cluster, while replica promotion (Option A) and Lambda restoration (Option B) are unrelated to immediate rollback operations.

Therefore, Option C, using Aurora Backtrack, best meets the requirement for same-cluster restoration and minimal downtime.

Reference:

AWS Cloud Operations & Database Management Guide Section: Using Aurora Backtrack for Fast In-Place Recovery

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QUESTION: 38

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An application runs on Amazon EC2 instances that are in an Auto Scaling group. A CloudOps engineer needs to implement a solution that provides a central storage location for errors that the application logs to disk. The solution must also provide an alert when the application logs an error.

What should the CloudOps engineer do to meet these requirements?

Deploy and configure the Amazon CloudWatch agent on the EC2 instances to log to a CloudWatch log group. Create a metric filter on the target CloudWatch log group. Create a CloudWatch alarm that publishes to an Amazon Simple Notification Service (Amazon SNS) topic that has an email subscription.
Create a cron job on the EC2 instances to identify errors and push the errors to an Amazon CloudWatch metric filter. Configure the filter to publish to an Amazon Simple Notification Service (Amazon SNS) topic that has an SMS subscription.
Deploy an AWS Lambda function that pushes the errors directly to Amazon CloudWatch Logs.
Configure the Lambda function to run every time the log file is updated on disk.
Create an Auto Scaling lifecycle hook that invokes an EC2-based script to identify errors. Configure the script to push the error messages to an Amazon CloudWatch log group when the EC2 instances scale in. Create a CloudWatch alarm that publishes to an Amazon Simple Notification Service (Amazon SNS) topic that has an email subscription when the number of error messages exceeds a threshold.

Answer(s): A

Explanation:

The AWS Cloud Operations and Monitoring documentation specifies that the Amazon CloudWatch Agent is the recommended tool for collecting system and application logs from EC2 instances. The agent pushes these logs into a centralized CloudWatch Logs group, providing durable storage and real-time monitoring.

Once the logs are centralized, a CloudWatch Metric Filter can be configured to search for specific error keywords (for example, "ERROR" or "FAILURE"). This filter transforms matching log entries into custom metrics. From there, a CloudWatch Alarm can monitor the metric threshold and publish notifications to an Amazon SNS topic, which can send email or SMS alerts to subscribed recipients.

This combination provides a fully automated, managed, and serverless solution for log aggregation and error alerting. It eliminates the need for manual cron jobs (Option B), custom scripts (Option D), or Lambda-based log streaming (Option C).

Reference:

AWS Cloud Operations & Monitoring Guide Collecting Application Logs and Creating Alarms Using CloudWatch Agent, Metric Filters, and SNS Notifications

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QUESTION: 39

A company's security policy prohibits connecting to Amazon EC2 instances through SSH and RDP. Instead, staff must use AWS Systems Manager Session Manager. Users report they cannot connect to one Ubuntu instance, even though they can connect to others.

What should a CloudOps engineer do to resolve this issue?

Add an inbound rule for port 22 in the security group associated with the Ubuntu instance.
Assign the AmazonSSMManagedInstanceCore managed policy to the EC2 instance profile for the Ubuntu instance.
Configure the SSM Agent to log in with a user name of "ubuntu".
Generate a new key pair, configure Session Manager to use this new key pair, and provide the private key to the users.

Answer(s): B

Explanation:

According to AWS Cloud Operations and Systems Manager documentation, Session Manager requires that each managed instance be associated with an IAM instance profile that grants Systems Manager core permissions. The required permissions are provided by the AmazonSSMManagedInstanceCore AWS-managed policy.

If this policy is missing or misconfigured, the Systems Manager Agent (SSM Agent) cannot communicate with the Systems Manager service, causing connection failures even if the agent is installed and running. This explains why other instances work--those instances likely have the correct IAM role attached.

Enabling port 22 (Option A) violates the company's security policy, while configuring user names (Option C) and key pairs (Option D) are irrelevant because Session Manager operates over secure API channels, not SSH keys.

Therefore, the correct resolution is to attach or update the instance profile with the AmazonSSMManagedInstanceCore policy, restoring Session Manager connectivity.

Reference:

AWS Cloud Operations & Systems Manager Guide Instance Profile Requirements for Session Manager Connectivity

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QUESTION: 40

A company deploys an application on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer (ALB). The company wants to protect the application from SQL injection attacks.

Which solution will meet this requirement?

Deploy AWS Shield Advanced in front of the ALB. Enable SQL injection filtering.
Deploy AWS Shield Standard in front of the AL Enable SQL injection filtering.
Deploy a vulnerability scanner on each EC2 instance. Continuously scan the application code.
Deploy AWS WAF in front of the ALB. Subscribe to an AWS Managed Rule for SQL injection filtering.

Answer(s): D

Explanation:

The AWS Cloud Operations and Security documentation confirms that AWS WAF (Web Application Firewall) is designed to protect web applications from application-layer threats, including SQL

injection, cross-site scripting (XSS), and other OWASP Top 10 vulnerabilities.

When integrated with an Application Load Balancer, AWS WAF inspects incoming traffic using rule groups. The AWS Managed Rules for SQL Injection Protection provide preconfigured, continuously updated filters that detect and block malicious SQL patterns.

AWS Shield (Standard or Advanced) defends against DDoS attacks, not application-layer SQL attacks, and vulnerability scanners (Option C) only detect, not prevent, exploitation.

Thus, Option D provides the correct, managed, and automated protection aligned with AWS best practices.

Reference:

AWS Cloud Operations & Security Guide Protecting Applications from SQL Injection with AWS WAF Managed Rules

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Free AWS Certified CloudOps Engineer - Associate SOA-C03 Exam Braindumps AWS Certified CloudOps Engineer - Associate Questions 36–40 (page: 9)

QUESTION: 36

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QUESTION: 38

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QUESTION: 39

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QUESTION: 40

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Free AWS Certified CloudOps Engineer - Associate SOA-C03 Exam Braindumps
AWS Certified CloudOps Engineer - Associate Questions 36–40 (page: 9)