Explore the essential concepts of autoscaling strategies and policies in cloud environments with this interactive quiz, focusing on scalability rules, triggers, and optimization techniques to help maintain optimal performance and cost-efficiency.
Which best describes autoscaling in a cloud environment?
Explanation: The correct answer is automatically adjusting computing resources based on demand, as autoscaling scales resources in real-time or according to policies. Manually increasing server capacity daily is not automated. Fixing the number of virtual machines at deployment contradicts the dynamic nature of autoscaling. Scheduling updates to software applications does not relate directly to changing resource levels.
If a company wants to add more instances when CPU usage exceeds 70% for 10 minutes, which type of policy are they using?
Explanation: A threshold-based policy increases or decreases resources when certain metrics, like CPU usage, reach a defined level. Time-based policy would scale at specific times, not based on usage. Random scaling strategy is not a recognized approach and lacks predictability. Instance mirroring typically refers to duplicating an environment, not scaling based on metrics.
When a system removes servers during periods of low demand, what is this process called?
Explanation: Scaling in means reducing the number of instances to match lower demand, making resources more efficient. Scale up refers to making individual resources more powerful, while scale out adds more instances. Load balancing distributes traffic and does not directly involve adding or removing capacity.
How does dynamic autoscaling differ from static scaling strategies?
Explanation: Dynamic autoscaling automatically responds to demand, while static scaling sets resource levels ahead of time, regardless of load. Shutting down the system is not required for autoscaling adjustments, making option two incorrect. Static scaling does not always decrease resources during high demand; this would cause downtime. Random scaling triggers do not define static scaling.
Which is a common benefit of using autoscaling policies in the cloud?
Explanation: Autoscaling helps reduce costs by only using the resources needed, thus avoiding expenses from idle resources. Doubling network latency is not a benefit and is undesirable. Allocating the maximum resources constantly increases costs. Not adjusting resources also fails to respond to real demand changes.
In response to increased user load, an application adds more virtual machines to handle traffic. What is this approach called?
Explanation: Scale out refers to adding more instances or machines to distribute load. Scale down and scale in both refer to reducing resources, not increasing them. Scale left is not a commonly used term in autoscaling.
What is the main purpose of setting a cooldown period in an autoscaling policy?
Explanation: A cooldown period ensures the system has time to stabilize after scaling before new scaling actions occur, thus preventing wasteful or excessive changes. Increasing the speed of instances is unrelated to cooldowns. Backups are a separate process. Disabling monitoring features would make autoscaling less effective.
Which metric would commonly trigger an autoscaling event for a web application?
Explanation: CPU utilization directly reflects system load and is commonly used to trigger autoscaling. Font size, mouse cursor color, and document type hold no relevance to workload or server demand and are not used as scaling metrics.
To automatically add instances every weekday morning regardless of usage, what policy type should you use?
Explanation: A scheduled policy performs scaling actions at specific times or intervals, fitting the scenario of weekday morning increases. Utilization-based policies depend on load, not time. Instant recovery is about restoring failed resources, not timed scaling. Throttling relates to controlling traffic or resource usage, not scheduling scaling.
Which scenario requires manual intervention even with autoscaling policies in place?
Explanation: Configuration errors often require human review and correction to prevent problems with autoscaling. Automatic resource changes, whether scaling out or in, should occur without manual input if policies are set correctly. Removing idle instances is also automatic. Only issues with policy setup or logic need intervention.