CompTIA DY0-001 Exam With Confidence Using Practice Dumps

→ 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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→ A LEFT JOIN ensures all rows from Table 1 (Top Movies) are preserved, even if there’s no matching actor data in Table 2. This matches the requirement to show all movies, enriched with actor information when available.

Why the other options are incorrect:

A: INNER JOIN would exclude movies without matching actor entries.

B: UNION combines distinct rows — not appropriate for matching columns between two tables.

C: INTERSECT shows only common movies — excludes unmatched top movies.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.2:“LEFT JOINs are used when all records from one table (primary) must be retained, even if there are no matching rows in the secondary table.”

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→ Pooling layers are used in Convolutional Neural Networks (CNNs) to reduce the spatial dimensions (width and height) of the feature maps. This helps in downsampling, reducing computational complexity, and controlling overfitting by summarizing the features (e.g., max pooling or average pooling).

Why the other options are incorrect:

B: Input layers receive raw data and do not perform downsampling.

C: Output layers generate the final prediction.

D: Hidden layers process data but do not specifically perform downsampling unless designed to do so (e.g., convolutional or pooling sublayers).

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.3:“Pooling layers are used to downsample feature maps and are critical in CNNs for reducing dimensions.”

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