AZ-305 Designing Microsoft Azure Infrastructure Solutions Exam

Recommend a Compute Solution for Batch Processing

Evaluate and Configure Azure Batch for Parallel Workloads

Azure Batch is a managed service designed to run large-scale, intrinsically parallel and high-performance computing (HPC) jobs without the need to install or manage cluster software. It provisions and scales a pool of compute nodes (virtual machines), installs applications, and schedules tasks across these nodes. You only pay for the underlying resources, such as VMs, storage, and networking, making it cost-effective for intermittent or large bursts of compute. Developers can integrate Batch via REST APIs, SDKs, or the Azure portal to build services for scenarios like Monte Carlo simulations or image processing.

When recommending a compute solution for batch processing, you must analyze batch job size, task concurrency, and execution duration. These factors guide your choice of VM series, node count, and autoscaling parameters. For example, you might select H-series VMs for MPI workloads or GPU-optimized series for rendering. It’s also critical to choose the right OS image and node communication model (classic vs. simplified) to balance complexity and security.

To meet performance and cost objectives, configure Azure Batch pools with an appropriate scheduling policy and node deallocation option. You can control how many tasks run per node and whether tasks are packed onto nodes or spread evenly. Automatic scaling formulas let you scale out during peak demand (for example, when $PendingTasks rises) and scale in when demand drops, with customizable intervals and deallocation modes like taskCompletion. Optionally, you can leverage low-priority (Spot) VMs to reduce costs further.

Finally, follow best practices for pool and node management to ensure reliability and efficiency. Avoid images or SKUs nearing end-of-life, use ephemeral OS disks for cost savings, and attach data disks only via idempotent start tasks. Implement node restart policies to recover from failures and use availability zones or virtual networks for high availability and secure communication. By carefully designing pools and autoscaling rules, you can achieve a robust, scalable compute solution for batch processing on Azure.

---

Conclusion

In summary, recommending a compute solution for batch processing with Azure involves evaluating key aspects like batch job size, task concurrency, execution duration, VM series choice, auto-scaling parameters, OS image selection, scheduling policy, node deallocation options, and leveraging low-priority VMs. Proper configuration and best practices in node management ensure optimal performance, cost efficiency, reliability, and security in running large-scale parallel workloads on Azure Batch.