DY0-001 Exam Dumps : CompTIA DataX Exam

→ 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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→ Regular expressions (regex) are powerful tools for pattern matching in text. They are ideal for extracting substrings, such as domains, parameters, or specific keywords from URLs or structured text fields.

Why the other options are incorrect:

B: NER is used to extract named entities (like names, places) — not substrings in structured text.

C: LLMs are overkill and not efficient for simple string matching tasks.

D: Find and replace is manual and non-scalable for large data sets.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 6.3:“Regular expressions provide a flexible method to extract patterns and substrings in structured or semi-structured text.”

Data Cleaning Handbook, Chapter 3:“Regex is the most effective tool for parsing text formats like URLs, emails, or custom tags.”

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→ Linear Discriminant Analysis (LDA) is a supervised classification method used to predict a categorical target (such as travel destination) based on multiple input features. It models decision boundaries between classes — which is appropriate when predicting a fixed set of destinations.

Why the other options are incorrect:

B: k-means is unsupervised and doesn't use labeled output like travel destination.

C: Latent Semantic Analysis is used for extracting relationships from textual data — not categorical prediction.

D: PCA reduces dimensionality but doesn’t classify.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.1:“Linear Discriminant Analysis is used when the response variable is categorical and the objective is classification.”

Classification Techniques Guide, Chapter 7:“LDA excels in multi-class prediction when the input data is continuous and the output is a known category.”

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