AZ-204 Develop Azure compute solutions Practice Question
You are designing a solution that uses Azure Batch for parallel processing of large datasets. Each task requires significant CPU and memory. You need to minimize compute costs while ensuring tasks complete within a deadline. Which pool configuration should you use?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Low-priority VMs with a task retry policy
Low-priority VMs (now called Spot VMs) offer significant cost savings but can be preempted. Using them with a task retry policy ensures completion. Dedicated VMs are more expensive.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A mix of dedicated and low-priority VMs without retry
Why it's wrong here
While combining dedicated and low-priority VMs can balance cost and reliability for different task types, omitting a task retry policy for tasks running on low-priority nodes introduces a critical vulnerability. Tasks executing on low-priority VMs are subject to preemption, meaning they can be terminated unexpectedly. Without an automatic retry mechanism, these preempted tasks will simply fail, leading to incomplete job execution, wasted compute cycles, and requiring manual intervention to restart, thereby undermining the solution's efficiency and reliability.
- ✓
Low-priority VMs with a task retry policy
Why this is correct
This approach is optimal for cost-effective large-scale parallel processing in Azure Batch. Low-priority VMs offer significant cost savings by utilizing surplus Azure capacity, making them ideal for workloads that can tolerate interruptions. The crucial addition of a task retry policy ensures that if a low-priority VM is preempted and a task is interrupted, Azure Batch automatically reschedules and restarts that task on another available node. This combination guarantees eventual task completion and maintains the overall reliability of the solution while dramatically reducing compute costs.
- ✗
Use Azure Container Instances instead of Batch
Why it's wrong here
Azure Container Instances (ACI) provide a fast and serverless way to run individual containers without managing underlying infrastructure, suitable for burstable workloads or small-scale container deployments. However, for large-scale parallel processing and high-performance computing (HPC) scenarios involving hundreds or thousands of compute nodes, ACI lacks the sophisticated job scheduling, auto-scaling, and cost optimization features inherent to Azure Batch. Managing such a large number of independent ACI instances would be significantly more complex and less cost-effective than leveraging Batch's integrated orchestration capabilities.
- ✗
Dedicated VMs only
Why it's wrong here
Utilizing only dedicated VMs in Azure Batch guarantees compute capacity and prevents task preemption, which is suitable for highly critical or long-running tasks that cannot tolerate any interruption. However, dedicated VMs come at a substantially higher cost compared to low-priority VMs. For typical parallel processing workloads where tasks are often independent, stateless, and can be restarted without significant overhead, exclusively using dedicated VMs would lead to excessive operational expenses, failing to leverage Azure's cost-optimization opportunities for elastic compute.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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