Which of the following environmental changes is most likely to resolve a memory constraint error...

When running a model on a distributed system, encountering memory constraint errors indicates that the current nodes in the cluster do not have enough memory to handle the model. The most scalable and immediate solution is:

→ Adding Nodes to a Cluster Deployment – This increases the total available memory and compute power. In distributed computing environments like Apache Spark or Hadoop, horizontal scaling via node addition is a standard remedy for resource bottlenecks, including memory limitations.

Why the other options are incorrect:

A. Containerizing doesn’t inherently solve memory issues unless paired with resource upgrades.

B. Cloud migration may offer more resources, but without scaling configuration, memory limits may persist.

C. Edge deployment is for low-latency, local processing – often with less memory, not more.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.2 (Infrastructure & Scaling):“To resolve memory limitations in distributed systems, scaling out by adding nodes is the most direct and cost-effective method.”

Data Engineering Fundamentals (Cloud/Distributed Systems):“Cluster resource constraints (e.g., memory) can be mitigated by increasing node count, enabling parallel execution and expanded memory pools.”

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