CompTIA DY0-001 Dumps Questions Answers

→ Compression is a CPU-intensive process because it requires encoding data into a smaller format, often involving complex algorithms. While memory use is usually moderate, CPU usage can spike significantly, especially during real-time compression or large dataset processing.

Why the other options are incorrect:

A: Library issues are possible but not the most likely issue in compression.

C: Most operating systems support common compression formats (e.g., .zip, .gz).

D: Memory usage is generally lower than CPU usage during compression.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.4:“Compression is compute-intensive and may result in increased CPU utilization, particularly on shared servers or during large batch processes.”

Cloud Data Engineering Guide, Chapter 9:“High CPU usage is a common bottleneck in data compression and decompression processes, especially at scale.”

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→ Extrapolated data refers to making predictions about data points that fall outside the observed range or distribution. Since the sample data (80%) is heavily skewed toward a small subset of professions and locations, predicting results for the remaining, unrepresented professions and regions involves extrapolation.

Why the other options are incorrect:

A: Interpolation occurs within the bounds of observed data — not the issue here.

C: In-sample data refers to training data, which is overrepresented in this case.

D: Out-of-sample data is a concern in generalization but extrapolation is more specific here.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.2:“Extrapolation introduces risk when models are used outside the range of data they were trained on, especially if certain subgroups are underrepresented.”

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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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