Cost Optimization practice questions

CloudWatch metrics show your EC2 instances have average CPU utilization around 10% with stable performance over several weeks. The application does not require additional headroom right now. What is the most effective cost-optimization action?

Trap 1: Increase the Auto Scaling desired capacity to add more instances

Adding more instances increases total compute capacity and typically increases cost, even if CPU utilization is low. Auto Scaling is intended to respond to demand changes; raising desired capacity without a demand need does not address the current overprovisioning.

Trap 2: Switch to Spot Instances immediately even though interruptions…

Switching to Spot is a pricing decision that introduces interruption risk. The scenario’s primary measurable issue is persistent underutilization; switching to Spot would be unnecessary and potentially risky given the stated interruption impact. Cost optimization here should first target the dominant cost lever: oversized compute.

Trap 3: Disable detailed monitoring to reduce CPU usage from the monitoring…

Reducing monitoring detail might lower monitoring charges, but it does not address the main driver of EC2 cost: unused instance capacity. Any CPU impact from monitoring is usually minor compared with the savings from downsizing instances.

A
Right-size the instances to a smaller size that matches the observed utilization
Right sizing reduces cost by matching instance capacity to actual demand. If average CPU is consistently low (around 10%) and performance is stable, it strongly indicates overprovisioning. Moving to a smaller instance (or a smaller capability within the same family) typically lowers hourly cost while maintaining sufficient capacity for the workload.
B
Increase the Auto Scaling desired capacity to add more instances
Why wrong: Adding more instances increases total compute capacity and typically increases cost, even if CPU utilization is low. Auto Scaling is intended to respond to demand changes; raising desired capacity without a demand need does not address the current overprovisioning.
C
Switch to Spot Instances immediately even though interruptions would impact users
Why wrong: Switching to Spot is a pricing decision that introduces interruption risk. The scenario’s primary measurable issue is persistent underutilization; switching to Spot would be unnecessary and potentially risky given the stated interruption impact. Cost optimization here should first target the dominant cost lever: oversized compute.
D
Disable detailed monitoring to reduce CPU usage from the monitoring agent
Why wrong: Reducing monitoring detail might lower monitoring charges, but it does not address the main driver of EC2 cost: unused instance capacity. Any CPU impact from monitoring is usually minor compared with the savings from downsizing instances.

Question 2mediummulti select

A company is migrating its on-premises workloads to AWS and wants to optimize costs. Which three strategies should the company implement to achieve a cost-optimized architecture? (Choose three.)

Trap 1: Provision additional EC2 instances to handle peak load at all…

Provisioning additional EC2 instances to handle peak load at all times is an inefficient and costly approach. This strategy leads to significant over-provisioning during off-peak periods, where a large portion of the allocated resources remains idle and unused. Consequently, the company incurs unnecessary expenses for compute capacity that is not actively contributing to workload performance, directly contradicting cost optimization goals.

Trap 2: Store all data in Amazon S3 Standard storage class to avoid any…

Storing all data exclusively in Amazon S3 Standard storage class is not an optimal cost-saving strategy, as S3 Standard does incur data retrieval costs, albeit typically lower than other classes for frequent access. For data that is accessed less frequently or archived, using lifecycle policies to transition objects to S3 Infrequent Access, Glacier, or Glacier Deep Archive would yield significant storage cost reductions, despite potentially higher retrieval fees or longer retrieval times.

Trap 3: Deploy all resources in a single Availability Zone to minimize data…

Deploying all resources within a single Availability Zone (AZ) to minimize data transfer costs is a critical misstep for production workloads. While it might reduce cross-AZ data transfer charges, this approach creates a single point of failure, severely compromising the application's high availability and fault tolerance. The potential cost savings are far outweighed by the increased risk of downtime and data loss in the event of an AZ-wide outage.

A
Use Reserved Instances or Savings Plans for predictable workloads to reduce costs compared to On-Demand pricing.
Using Reserved Instances (RIs) or Savings Plans (SPs) is a highly effective cost optimization strategy for workloads with predictable, consistent resource usage over a 1-year or 3-year term. By committing to a specific instance family, region, or compute usage, customers receive substantial discounts, often up to 72% compared to On-Demand pricing, significantly reducing the total cost of ownership for stable applications.
B
Provision additional EC2 instances to handle peak load at all times, ensuring maximum performance.
Why wrong: Provisioning additional EC2 instances to handle peak load at all times is an inefficient and costly approach. This strategy leads to significant over-provisioning during off-peak periods, where a large portion of the allocated resources remains idle and unused. Consequently, the company incurs unnecessary expenses for compute capacity that is not actively contributing to workload performance, directly contradicting cost optimization goals.
C
Implement auto scaling to match capacity with demand, avoiding over-provisioning and reducing waste.
Implementing Amazon EC2 Auto Scaling is a fundamental cost optimization technique that dynamically adjusts compute capacity to match actual demand. By automatically launching or terminating instances based on predefined metrics, Auto Scaling prevents both over-provisioning during low demand and under-provisioning during peak loads, ensuring optimal resource utilization and significantly reducing wasted expenditure on idle resources.
D
Use Spot Instances for fault-tolerant, flexible workloads to achieve significant cost savings.
Leveraging Spot Instances offers the most substantial cost savings, often up to 90% compared to On-Demand prices, for fault-tolerant and flexible workloads. These instances utilize unused EC2 capacity and are suitable for batch processing, stateless applications, or development/testing environments that can tolerate interruptions, as AWS can reclaim them with a two-minute warning.
E
Store all data in Amazon S3 Standard storage class to avoid any data retrieval costs.
Why wrong: Storing all data exclusively in Amazon S3 Standard storage class is not an optimal cost-saving strategy, as S3 Standard does incur data retrieval costs, albeit typically lower than other classes for frequent access. For data that is accessed less frequently or archived, using lifecycle policies to transition objects to S3 Infrequent Access, Glacier, or Glacier Deep Archive would yield significant storage cost reductions, despite potentially higher retrieval fees or longer retrieval times.
F
Deploy all resources in a single Availability Zone to minimize data transfer costs.
Why wrong: Deploying all resources within a single Availability Zone (AZ) to minimize data transfer costs is a critical misstep for production workloads. While it might reduce cross-AZ data transfer charges, this approach creates a single point of failure, severely compromising the application's high availability and fault tolerance. The potential cost savings are far outweighed by the increased risk of downtime and data loss in the event of an AZ-wide outage.

Question 3mediummultiple choice

An Auto Scaling group for a background worker runs EC2 instances continuously. Over the last 30 days, CloudWatch shows sustained CPU utilization around 6% with no memory pressure, and queue processing latency meets all SLAs. The team wants to lower monthly cost with minimal risk. What is the best next action?

Trap 1: Increase the instance size to reduce CPU throttling risk

The metrics show sustained low CPU with no reported throttling risk. Increasing size would likely increase cost immediately without addressing an existing performance problem.

Trap 2: Switch the group to Spot Instances to reduce cost without changing…

Spot could reduce compute price, but it introduces interruption risk and additional operational complexity. When the primary waste is clearly overprovisioned capacity, downsizing is the more direct and lower-risk cost optimization.

Trap 3: Buy Reserved Instances with a long term commitment before making…

Purchasing capacity commitments can reduce unit price, but it does not fix the cost waste from running much larger-than-needed instances. Since the instance utilization indicates overprovisioning, right sizing should come first to maximize savings with minimal risk.

A
Increase the instance size to reduce CPU throttling risk
Why wrong: The metrics show sustained low CPU with no reported throttling risk. Increasing size would likely increase cost immediately without addressing an existing performance problem.
B
Perform right sizing by downsizing to a smaller instance family/size and validate SLAs
Right sizing uses actual utilization to remove overprovisioning. With low CPU and no memory pressure and SLAs already met, downsizing (while validating under load and during a controlled rollout) is the safest way to reduce waste.
C
Switch the group to Spot Instances to reduce cost without changing instance sizing
Why wrong: Spot could reduce compute price, but it introduces interruption risk and additional operational complexity. When the primary waste is clearly overprovisioned capacity, downsizing is the more direct and lower-risk cost optimization.
D
Buy Reserved Instances with a long term commitment before making any sizing changes
Why wrong: Purchasing capacity commitments can reduce unit price, but it does not fix the cost waste from running much larger-than-needed instances. Since the instance utilization indicates overprovisioning, right sizing should come first to maximize savings with minimal risk.

Question 4easymultiple choice

A media company runs a batch job that processes image thumbnails. The job can be restarted from checkpoints and does not have user-facing SLAs. The batch capacity can tolerate interruptions. Which EC2 purchasing option is the best cost optimization choice?

Trap 1: Use On-Demand Instances because interruptions are not allowed for…

On-Demand is reliable but usually more expensive than Spot for interruptible batch workloads.

Trap 2: Purchase Reserved Instances because they provide a discount…

Reserved Instances reduce cost, but they generally assume steady usage and are not optimized for interruptible jobs.

Trap 3: Buy Savings Plans because they guarantee capacity and remove the…

Savings Plans are for discounted steady compute, not for eliminating interruption risk on interruptible workloads.

A
Use On-Demand Instances because interruptions are not allowed for production workloads.
Why wrong: On-Demand is reliable but usually more expensive than Spot for interruptible batch workloads.
B
Use EC2 Spot Instances, accepting the possibility of interruptions and using checkpoints to resume.
Spot Instances are typically the cheapest option for workloads that can tolerate interruptions with recovery.
C
Purchase Reserved Instances because they provide a discount regardless of the workload timing.
Why wrong: Reserved Instances reduce cost, but they generally assume steady usage and are not optimized for interruptible jobs.
D
Buy Savings Plans because they guarantee capacity and remove the risk of interruptions entirely.
Why wrong: Savings Plans are for discounted steady compute, not for eliminating interruption risk on interruptible workloads.

Question 5easymultiple choice

A startup has a stable production web service that runs continuously (24/7) on AWS. They have consistent compute requirements for the next 1 year, but the instance size and family might change as they optimize performance. To reduce cost while maintaining flexibility across instance types, which purchasing option should they consider?

Trap 1: Reserved Instances with a fixed instance type

Fixed instance-type Reserved Instances can limit flexibility if the team needs to change instance families.

Trap 2: Spot Instances

Spot is interruption-capable and may not be appropriate for always-on production without added complexity.

Trap 3: On-Demand Instances

On-Demand provides maximum flexibility but generally lacks the savings of commitment-based plans.

A
Compute Savings Plans
Compute Savings Plans discount compute usage while allowing flexibility across instance families, sizes, and even some services.
B
Reserved Instances with a fixed instance type
Why wrong: Fixed instance-type Reserved Instances can limit flexibility if the team needs to change instance families.
C
Spot Instances
Why wrong: Spot is interruption-capable and may not be appropriate for always-on production without added complexity.
D
On-Demand Instances
Why wrong: On-Demand provides maximum flexibility but generally lacks the savings of commitment-based plans.

Question 6mediummultiple choice

A media company runs a 24/7 recommendation engine on EC2 in one AWS Region. The workload is interruption-intolerant, and the team expects steady usage but may change instance families and sizes during planned optimizations. Compared to the current On-Demand setup, they want the lowest cost while avoiding the rigidity of locking to a specific instance type.

What should the solutions architect recommend?

Trap 1: Switch the instances to Spot Instances and use interruption…

Spot Instances provide strong discounts, but interruption tolerance is required, which the workload lacks.

Trap 2: Purchase a Standard Reserved Instance tied to a single specific…

Standard RIs can reduce cost, but they are less flexible when the team needs instance-type changes.

Trap 3: Keep On-Demand and rely on Auto Scaling to reduce capacity when…

Auto Scaling can help, but for interruption-intolerant 24/7 usage it usually cannot match Savings Plans.

A
Switch the instances to Spot Instances and use interruption handling because it is the largest discount.
Why wrong: Spot Instances provide strong discounts, but interruption tolerance is required, which the workload lacks.
B
Purchase a Compute Savings Plan for the expected steady hourly usage in that Region.
Compute Savings Plans discount the usage while allowing flexibility across instance families and sizes in the Region.
C
Purchase a Standard Reserved Instance tied to a single specific instance type for the next 3 years.
Why wrong: Standard RIs can reduce cost, but they are less flexible when the team needs instance-type changes.
D
Keep On-Demand and rely on Auto Scaling to reduce capacity when utilization is low.
Why wrong: Auto Scaling can help, but for interruption-intolerant 24/7 usage it usually cannot match Savings Plans.

Question 7hardmulti select

A retailer runs a reporting-heavy relational app on Amazon RDS MySQL. Peak dashboard traffic lasts only three hours each day, but the database is sized for the peak all day. The business wants lower cost without rewriting the application. Which three actions are best? Select three.

Trap 1: Migrate to DynamoDB immediately because every relational workload…

Incorrect. DynamoDB is not a drop-in replacement for a relational reporting app. A rewrite would be required, and the query pattern may not fit key-value access at all.

Trap 2: Increase provisioned IOPS permanently so the monthly bill drops.

Incorrect. More provisioned IOPS increases cost, not decreases it. The issue is overprovisioned compute and topology, not a lack of enough disk performance everywhere.

A
Right-size the writer based on actual utilization instead of peak guesses.
Correct. Right-sizing removes waste from the always-on primary instance. If the writer is sized for real load rather than a worst-case assumption, the company pays for less unused compute.
B
Add read replicas and direct dashboard traffic away from the writer.
Correct. Read replicas let read-heavy dashboards scale without forcing the writer to be oversized for every query. This improves scaling economics while keeping the application relational and largely unchanged.
C
Evaluate Aurora MySQL if the current replica-heavy design would be cheaper there.
Correct. Aurora can be more cost-efficient than a replica-heavy RDS topology because storage is shared across the cluster and replicas are simpler to operate. It is a reasonable tradeoff to evaluate when read scaling is the main issue.
D
Migrate to DynamoDB immediately because every relational workload is more expensive.
Why wrong: Incorrect. DynamoDB is not a drop-in replacement for a relational reporting app. A rewrite would be required, and the query pattern may not fit key-value access at all.
E
Increase provisioned IOPS permanently so the monthly bill drops.
Why wrong: Incorrect. More provisioned IOPS increases cost, not decreases it. The issue is overprovisioned compute and topology, not a lack of enough disk performance everywhere.