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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→ Euclidean distance is the most intuitive distance metric. It measures the shortest "straight-line" distance between two points in Euclidean space. This is typically used in KNN and clustering when features are continuous and appropriately scaled.

Why the other options are incorrect:

A: "Radial" isn’t a standard distance metric; may refer vaguely to radial basis functions.

C: Cosine measures the angle (orientation) between vectors — not straight-line distance.

D: Manhattan distance sums the absolute differences across dimensions — visualized as block-like (taxicab) paths, not direct lines.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.4:“Euclidean distance is the default metric in KNN for measuring straight-line proximity in feature space.”

Data Mining Techniques, Chapter 3:“Euclidean distance represents the shortest path between two points and is widely used in distance-based learning algorithms.”

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→ Multicollinearity occurs when independent variables are highly correlated with each other. This can distort coefficient estimates and reduce model interpretability. A correlation matrix is the primary tool used to detect it.

Why the other options are incorrect:

A & C: Under/oversampling relate to class imbalance, not variable correlation.

D: Overfitting is related to model complexity, not directly observable via a correlation matrix.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.2:“Correlation matrices are used to detect multicollinearity — high correlations among predictors that may destabilize models.”