Amazon AWS Certified SysOps Administrator - Associate Exam Dumps & Practice Test Questions

Question 1:

A company operates an internal web application hosted on Amazon EC2 instances behind an Application Load Balancer. These instances are part of an EC2 Auto Scaling group that runs in only one Availability Zone. 

As a SysOps administrator, what should you do to improve the application's high availability?

A. Raise the maximum number of instances in the Auto Scaling group to handle peak demand.
B. Increase the minimum number of instances in the Auto Scaling group to meet peak capacity.
C. Modify the Auto Scaling group to launch instances in a second Availability Zone within the same AWS Region.
D. Change the Auto Scaling group to launch instances in a second AWS Region's Availability Zone.

Correct Answer: C

Explanation:

Ensuring high availability means the application should remain operational and accessible even if one part of the infrastructure fails. In AWS, high availability is typically achieved by deploying resources across multiple Availability Zones (AZs) within the same Region. Each AZ is an isolated location within a Region designed to be independent from failures in other AZs.

Currently, the application is deployed in a single Availability Zone, which creates a single point of failure. If that AZ experiences an outage, the entire application could become unavailable.

Let's examine the options:

Option A suggests increasing the maximum number of instances to handle more users during peak times. While this improves scalability and performance under load, it does not provide fault tolerance if the single AZ fails. So, this option does not solve the high availability requirement.

Option B focuses on increasing the minimum number of instances, ensuring more instances are always running. This maintains capacity but, like Option A, it still keeps instances in one AZ, leaving the system vulnerable to AZ outages. Therefore, it does not meet high availability needs.

Option C proposes configuring the Auto Scaling group to span multiple Availability Zones within the same Region. This is the correct solution. Distributing instances across at least two AZs ensures that if one AZ goes down, traffic is routed to healthy instances in the other AZ via the Application Load Balancer. This setup offers fault tolerance and meets the high availability goal.

Option D involves launching instances in a second AWS Region. While cross-region deployment adds geographic redundancy and disaster recovery, it introduces complexity such as managing data replication and failover between regions. For high availability within a single region, this is unnecessary and overcomplicates the architecture.

In conclusion, to make the web application highly available, you should enable the Auto Scaling group to launch instances in multiple Availability Zones within the same Region, as described in Option C.

Question 2:

A company runs a website using multiple Amazon EC2 instances within an Auto Scaling group. Users report slow response times during peak weekend hours from 6 PM to 11 PM. 

As a SysOps administrator, what is the most operationally efficient way to improve performance during these specific peak periods?

A. Use an Amazon EventBridge scheduled rule to trigger an AWS Lambda function that increases the desired capacity before peak times.
B. Set up a scheduled scaling action with recurrence to adjust desired capacity before and after peak hours.
C. Create a target tracking scaling policy that adds instances when memory usage exceeds 70%.
D. Adjust the cooldown period of the Auto Scaling group to modify desired capacity around peak times.

Correct answer: B

Explanation:

The issue is slow website response during predictable, recurring peak times—weekends from 6 PM to 11 PM. The goal is to efficiently scale out the EC2 instances in the Auto Scaling group ahead of these peak hours and scale back afterward.

Option A proposes triggering a Lambda function via EventBridge to adjust capacity. While this works, it adds unnecessary complexity because it requires managing Lambda code and event triggers. It’s less operationally streamlined since it uses additional AWS services instead of native Auto Scaling features.

Option B recommends configuring scheduled scaling actions within Auto Scaling itself. Scheduled scaling lets you define recurring times when the desired capacity automatically adjusts, perfectly matching the known peak window. This is the simplest and most operationally efficient method since it uses native functionality, requires minimal management overhead, and is fully automated.

Option C suggests a target tracking policy based on memory usage. While this can dynamically scale based on load, memory utilization is not always a reliable indicator of peak web traffic, and it reacts after utilization crosses thresholds rather than proactively adjusting capacity for known peak times. It may not provide timely scaling.

Option D discusses changing the cooldown period, which controls the wait time between scaling actions but doesn’t proactively scale based on time or load. Adjusting cooldown periods won’t directly solve the problem of ensuring enough instances during peak hours.

In summary, scheduling scaling actions to increase and decrease capacity aligned with known peak times offers the most operational efficiency by leveraging built-in Auto Scaling capabilities without extra complexity. Hence, B is the best solution.

Question 3:

A company has a website running on Amazon EC2 instances behind an Application Load Balancer (ALB). The website is delivered via an Amazon CloudFront distribution that uses the ALB as its origin. A Route 53 CNAME record routes traffic to CloudFront. 

However, mobile users are seeing the desktop version of the site instead of the mobile version. What should a SysOps administrator do to fix this?

A. Configure the CloudFront distribution’s cache behavior to forward the User-Agent header.
B. Add a User-Agent header to the list of custom origin headers in CloudFront.
C. Enable IPv6 on the ALB and update CloudFront to use the dualstack endpoint.
D. Enable IPv6 on CloudFront and update the Route 53 record to the dualstack endpoint.

Correct answer: A

Explanation:

The problem is that mobile users receive the desktop version of the website, which usually happens when the backend server cannot detect the type of device making the request. Typically, websites use the User-Agent HTTP header to determine whether a visitor is using a mobile device or desktop, delivering a tailored experience accordingly.

Amazon CloudFront caches content at edge locations to improve performance. By default, CloudFront doesn’t forward all HTTP headers to the origin to optimize caching. Importantly, CloudFront does not forward the User-Agent header unless explicitly configured, so the ALB receives requests without device identification information. Consequently, the backend serves the default desktop site.

Option A fixes this by forwarding the User-Agent header in CloudFront’s cache behavior settings. This lets the ALB receive the User-Agent, enabling the web servers behind the ALB to correctly detect the client device and serve the appropriate version of the site.

Option B suggests adding the User-Agent as a custom origin header. Custom origin headers are static and set by CloudFront itself, so this doesn’t help forward the dynamic User-Agent value from the client to the origin. Thus, this approach is ineffective.

Options C and D focus on enabling IPv6 and changing endpoints to dualstack (supporting IPv4 and IPv6). While IPv6 support is useful, it has no bearing on whether the backend detects mobile devices correctly, so these do not address the core issue.

In conclusion, forwarding the User-Agent header via CloudFront’s cache behavior is essential for device detection and delivering the correct mobile or desktop version of the website. Therefore, the best solution is A.

Question 4:

Which action should a SysOps administrator take to ensure that AWS CloudTrail is automatically reactivated immediately if it is ever disabled, without having to write any custom code?

A. Add the AWS account to AWS Organizations and enable CloudTrail from the management account.
B. Create an AWS Config rule that triggers when CloudTrail settings change and apply the built-in AWS-ConfigureCloudTrailLogging automatic remediation.
C. Create an AWS Config rule that triggers on CloudTrail changes and configure it to invoke an AWS Lambda function that re-enables CloudTrail.
D. Set up an Amazon EventBridge rule that runs hourly to execute an AWS Systems Manager Automation document which enables CloudTrail.

Correct answer: B

Explanation:

The requirement is to automatically re-enable AWS CloudTrail immediately if it becomes disabled, and importantly, to do this without writing custom code. The best AWS service for continuous compliance monitoring and remediation without custom scripts is AWS Config. AWS Config enables you to create rules that check the compliance state of AWS resources and trigger automatic remediation actions.

Option B is the correct answer because it leverages a predefined AWS Config managed rule that watches CloudTrail configuration changes. When this rule detects CloudTrail is disabled, it automatically applies the AWS-ConfigureCloudTrailLogging remediation action, which re-enables CloudTrail. This solution uses built-in functionality and requires no custom Lambda functions or additional scripting.

Option A is not correct because while AWS Organizations allow centralized management and auditing, simply enabling CloudTrail in the management account does not guarantee immediate reactivation if CloudTrail is manually disabled in a member account.

Option C requires creating a Lambda function to re-enable CloudTrail. This involves writing and maintaining custom code, which contradicts the requirement.

Option D uses Amazon EventBridge scheduled events combined with Systems Manager Automation. While this could work, it’s more complex, less immediate (hourly checks), and still involves setting up more components compared to AWS Config’s event-driven remediation.

In summary, AWS Config’s managed rule with automatic remediation (Option B) provides the simplest, immediate, and code-free way to ensure CloudTrail remains enabled.

Question 5:

A company hosts its website on Amazon EC2 instances behind an Application Load Balancer and manages its domain using Amazon Route 53. They want to point the root domain (zone apex) to this website. 

Which DNS record type should they use?

A. AAAA record for the root domain
B. A record for the root domain
C. CNAME record for the root domain
D. Alias record for the root domain

Correct answer: D

Explanation:

When managing DNS for a domain, the zone apex (root domain, e.g., example.com) requires special handling when pointing it to AWS resources like an Application Load Balancer (ALB). Unlike subdomains, the root domain cannot use a CNAME record due to DNS standards.

Option D — an Alias record — is a Route 53-specific feature that allows you to point the root domain directly to AWS resources such as ALBs, CloudFront distributions, or S3 buckets without needing an IP address. Alias records behave like A records but can point to AWS resource DNS names, automatically resolving to the correct IP addresses behind the scenes.

Option A (AAAA record) maps to IPv6 addresses. ALBs do not have static IPv6 addresses that can be used this way, so this is not appropriate.

Option B (A record) requires static IP addresses, but ALBs do not have fixed IPs, making this unsuitable.

Option C (CNAME record) cannot be used at the zone apex according to DNS rules, so it is invalid for the root domain.

Therefore, to meet the requirement of pointing the root domain to an ALB-hosted website in Route 53, the Alias record (Option D) is the correct and recommended choice because it complies with DNS standards and integrates directly with AWS infrastructure.

Question 6:

Which of the following actions will guarantee that all objects uploaded to an Amazon S3 bucket are encrypted? (Select two.)

A. Use AWS Shield to prevent unencrypted objects from being stored in the bucket.
B. Use S3 Object Access Control Lists (ACLs) to deny uploads of unencrypted objects.
C. Enable default encryption on the S3 bucket so that all uploaded objects are encrypted automatically.
D. Use Amazon Inspector to check uploaded objects and verify their encryption status.
E. Use an S3 bucket policy to deny uploads of objects that are not encrypted.

Correct answers: C, E

Explanation:

Ensuring all objects uploaded to an S3 bucket are encrypted involves both enforcing encryption automatically and blocking uploads that do not meet encryption requirements.

Option C is a core method: enabling Amazon S3 default encryption configures the bucket to automatically encrypt any object uploaded without the client needing to specify encryption explicitly. You can choose either SSE-S3 (server-side encryption managed by S3) or SSE-KMS (using AWS Key Management Service). This ensures encryption happens by default.

Option E adds another layer of security by using an S3 bucket policy to explicitly deny any upload request for objects that do not include encryption headers. This means unencrypted objects are outright rejected by the bucket, enforcing encryption policy compliance.

Option A is incorrect because AWS Shield is designed for DDoS protection and does not have any functionality related to encryption enforcement.

Option B is invalid since S3 ACLs control access permissions but do not enforce encryption on uploaded objects.

Option D is incorrect because Amazon Inspector is used for security assessments of EC2 instances and other AWS resources; it does not inspect S3 object encryption status.

By combining default encryption (C) and bucket policies that deny unencrypted uploads (E), you ensure that encryption is enforced both automatically and through access control, effectively guaranteeing all objects stored in the bucket are encrypted.

Question 7:

What steps should a SysOps administrator take to fix random user logouts from a stateful web application deployed behind an Application Load Balancer (ALB) and Amazon CloudFront?

A. Switch the ALB target group’s load balancing algorithm to least outstanding requests
B. Enable cookie forwarding in the CloudFront distribution’s cache behavior
C. Enable header forwarding in the CloudFront distribution’s cache behavior
D. Activate group-level stickiness on the ALB listener rule
E. Enable sticky sessions on the ALB target group

Correct Answer: B, E

Explanation:

The issue presented is that users are being randomly logged out from a stateful web application hosted on EC2 instances behind an ALB, with CloudFront acting as a content delivery layer. These random logouts typically occur because session persistence—often called sticky sessions—is not correctly maintained. This means the user’s requests may be routed to different backend instances, causing loss of session state.

Let’s analyze each option:

Option A: The “least outstanding requests” routing algorithm balances traffic based on which target has the fewest pending requests, optimizing load but not session affinity. It does not guarantee that a user's session sticks to the same backend instance, so it won’t prevent random logouts. Hence, it’s ineffective here.

Option B: Cookie forwarding in CloudFront ensures that session cookies sent by clients are forwarded through CloudFront to the ALB. Without this, CloudFront could cache responses or route requests in a way that breaks session stickiness. Forwarding cookies is essential so that the ALB can read them and maintain session persistence. This directly addresses the random logout issue.

Option C: Header forwarding involves forwarding HTTP headers to the backend. While useful for certain routing or authentication scenarios, it doesn’t influence session stickiness or persistence. Thus, it won’t resolve the logout problem.

Option D: Group-level stickiness on the ALB listener rule isn’t typically how session affinity is configured; stickiness is normally enabled at the target group level. So this won’t be an effective or standard solution.

Option E: Sticky sessions at the ALB target group level ensure that once a user’s session is established on a particular backend instance, subsequent requests are routed there. This is crucial for stateful apps relying on local session data, preventing random logouts.

The combination of forwarding cookies in CloudFront (to preserve session data) and enabling sticky sessions on the ALB target group (to maintain session affinity) is the best approach. This ensures session persistence is preserved end-to-end, preventing users from being randomly logged out.

Question 8:

How should a SysOps administrator fix the "too many connections" errors that happen when an AWS Lambda function connects to an Amazon RDS MySQL database, assuming the database is already set to its maximum connection limit?

A. Create a read replica and use Route 53 weighted DNS to distribute traffic
B. Deploy Amazon RDS Proxy and modify the Lambda connection string to use it
C. Increase the max_connect_errors parameter in the RDS parameter group
D. Raise the reserved concurrency setting for the Lambda function

Correct Answer: B

Explanation:

The problem described involves “too many connections” errors in an environment where AWS Lambda connects to an Amazon RDS MySQL database. The key detail is that the database’s max_connections parameter is already set to its maximum value, so increasing that limit is not possible.

Option A: Creating a read replica and distributing traffic with Route 53 may improve read scalability but does not address connection limits. Both the primary and the replica have their own connection limits, and simply splitting traffic doesn’t prevent the Lambda function from opening too many concurrent connections to either database. This approach is unrelated to connection management and thus doesn’t resolve the error.

Option B: Using Amazon RDS Proxy is the optimal solution here. RDS Proxy acts as a connection pooler, managing and reusing database connections efficiently on behalf of Lambda functions. Instead of every Lambda invocation opening a new direct connection, RDS Proxy maintains a pool of persistent connections to the database, drastically reducing the total number of active connections. This reduces connection overload and mitigates the “too many connections” error. Changing the Lambda function’s connection string to point to the RDS Proxy endpoint implements this solution.

Option C: The max_connect_errors parameter controls how many failed connection attempts from a host are allowed before blocking further attempts from that host. It has no impact on the total number of simultaneous connections allowed, so increasing it won’t fix connection limit errors.

Option D: Increasing the Lambda function’s reserved concurrency allows more concurrent Lambda executions but doesn’t solve the root problem: the database cannot handle that many connections at once. This could worsen the problem by allowing even more concurrent Lambda invocations trying to connect simultaneously.

Amazon RDS Proxy is designed precisely for this use case—efficiently managing and pooling database connections for serverless applications like Lambda. It reduces the connection load on the database, preventing "too many connections" errors without requiring changes to the database configuration. Therefore, Option B is the best and most effective solution.

Question 9:

A SysOps administrator needs to deploy an application across 10 Amazon EC2 instances with high availability. The instances must be hosted on separate physical hardware. 

Which approach should the administrator take to meet these requirements?

A. Launch the instances in a cluster placement group within a single AWS Region.
B. Launch the instances in a partition placement group across multiple AWS Regions.
C. Launch the instances in a spread placement group across multiple AWS Regions.
D. Launch the instances in a spread placement group within a single AWS Region.

Correct Answer: D

Explanation:

To ensure high availability and reduce the risk of hardware-related failures, placing EC2 instances on distinct physical hardware is crucial. AWS provides different placement group strategies to optimize how instances are distributed within infrastructure.

The spread placement group is specifically designed to place instances on unique, separate hardware to minimize correlated failures. This makes it the ideal choice for applications requiring high availability by distributing instances across distinct underlying hardware within the same Availability Zone or region.

Looking at other options:

• Cluster placement groups (Option A) collocate instances closely to achieve low network latency and high throughput, which is optimal for high-performance computing tasks. However, instances in a cluster group may reside on the same hardware rack or physical hosts, increasing the risk that a single hardware failure impacts multiple instances. This does not fulfill the requirement of isolating instances on different hardware.

• Partition placement groups (Option B) divide instances into logical partitions to isolate failures within a single region, but using multiple regions complicates deployment and is not necessary here. Partition groups don’t inherently guarantee unique hardware distribution across regions and introduce unnecessary latency and complexity.

• Spread placement groups across multiple regions (Option C) ensure distinct hardware but introduce cross-region latency and complexity, which is not required by the problem statement.

Therefore, Option D—launching instances into a spread placement group within a single AWS Region—is the best way to ensure that each EC2 instance runs on separate hardware while keeping high availability intact. This strategy balances fault isolation with simplicity and optimal performance.

Question 10:

A SysOps administrator is troubleshooting a CloudFormation template that launches multiple Amazon EC2 instances. The template works fine in the us-east-1 region but fails in us-west-2 with an error stating AMI [ami-12345678] does not exist. 

How should the administrator modify the template to make it functional across all AWS regions?

A. Copy the AMI from the source region to the destination region and assign it the same AMI ID.
B. Modify the CloudFormation template to specify the region code within the fully qualified AMI ID.
C. Change the template to allow users to select an AMI from a drop-down list using AWS::EC2::AMI::ImageID.
D. Add the AMI IDs for each region to the Mappings section of the template and reference them accordingly.

Correct Answer: D

Explanation:

Amazon Machine Image (AMI) IDs are unique to each AWS region. An AMI created or referenced in one region, such as us-east-1, will have a different AMI ID or might not exist at all in another region, like us-west-2. This leads to errors when using CloudFormation templates that hard-code AMI IDs and attempt to launch resources in different regions.

The correct way to handle this is to use the Mappings section in the CloudFormation template. Mappings allow you to define key-value pairs where the key is the region name, and the value is the corresponding AMI ID for that region. Within the template, you then use functions like Fn::FindInMap to dynamically select the correct AMI based on the region where the stack is being deployed. This ensures the template is region-agnostic and works seamlessly across all AWS regions.

Why the other options are not ideal:

• Copying the AMI and assigning the same ID (Option A) is not possible because AMI IDs are region-specific and auto-generated by AWS. Copying an AMI to another region results in a new AMI ID. The template still needs to reference the correct ID per region.

• Specifying the region code within the AMI ID (Option B) is unsupported since AMI IDs do not contain or allow region codes embedded inside them.

• Allowing user input of AMI ID via a drop-down (Option C) is not a native CloudFormation feature, and even if attempted, it introduces manual overhead and is prone to error, defeating the purpose of automation.

In conclusion, updating the CloudFormation template’s Mappings section (Option D) to include region-specific AMI IDs is the best practice to ensure the template functions correctly in every AWS region.