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.”

—

→ LASSO (Least Absolute Shrinkage and Selection Operator) regression performs both variable selection and regularization by adding an L1 penalty to the loss function. It shrinks less important feature coefficients to zero, effectively performing feature selection — perfect for identifying the most influential predictors.

Why the other options are incorrect:

A: OLS uses all predictors and doesn’t perform feature selection.

B: Ridge regression applies an L2 penalty, shrinking coefficients but keeping all predictors.

C: Weighted least squares adjusts for heteroscedasticity but doesn’t reduce variable count.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.3:“LASSO performs feature selection by zeroing out coefficients of less significant predictors.”

Statistical Learning Textbook, Chapter 6:“LASSO regression is ideal when model interpretability and variable reduction are important.”

—

→ The graph represents a classic “elbow curve,” which is often used in clustering (e.g., k-means) to help determine the optimal number of clusters. The point where the curve starts to level off (the “elbow”) reflects the best trade-off between model accuracy and processing efficiency.

In this graph, the elbow visually occurs around k = 10. Beyond that, the processing time continues to decrease, but the marginal gain in clustering quality (or drop in processing time) diminishes.

Why the other options are incorrect:

A: k = 2 underfits the data — too few clusters.

C & D: k = 15 or 20 provides minimal additional benefit in processing but may overcomplicate the model.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.2:“The elbow method identifies the optimal number of clusters where the rate of improvement drops significantly.”

—