DY0-001 Exam Dumps : CompTIA DataX Exam

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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→ GPUs (Graphics Processing Units) are optimized for parallel computations, which are essential for training deep neural networks. These models involve massive matrix operations across multiple layers, making GPUs significantly faster than CPUs in deep learning tasks.

Why the other options are incorrect:

B: Clustering (e.g., k-means) can benefit from acceleration but doesn’t usually require GPU-level computation.

C: NLP tasks may use GPUs if they involve deep learning (e.g., transformers), but the correct choice is the model type.

D: Tree-based models (e.g., decision trees, random forests) typically run efficiently on CPUs.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.3:“Deep learning models, such as neural networks, are computationally intensive and commonly require GPUs for efficient training.”

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→ Non-technical business stakeholders value outcome-oriented visuals (charts, dashboards) and the purpose/justification for the modeling work. These artifacts directly communicate impact without overwhelming technical complexity.

Why the other options are incorrect:

C & D: Too technical for a non-technical audience.

E: Useful, but may be too detailed depending on the level of abstraction desired.

F: Data dictionary is better suited for technical handoff — not executive review.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.5:“Business-oriented presentations should emphasize clear visualizations, insights, and executive summaries of model goals.”

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