DY0-001 Exam Dumps - CompTIA DataX Exam

→ For executive-level presentations, the focus should be on strategic outcomes. Therefore, concise results, clear actionable recommendations, visual summaries (charts), and minimal justifications are best. Technical details such as p-values, code, or full methods are too granular.

Why the other options are incorrect:

A: Too method-heavy for executive audiences.

C: Includes code reviews — not suitable for a CEO.

D: Overly technical for high-level stakeholders.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.5:“Executive communication should focus on outcome-driven recommendations, high-level insights, and actionable visuals.”

Harvard Business Review – Communicating Data to Executives:“Avoid technical detail. Use visuals and clearly stated recommendations supported by business-focused justifications.”

→ Edit distance (also known as Levenshtein distance) measures how many single-character edits (insertions, deletions, or substitutions) are needed to transform one string into another. It’s a common metric for assessing string similarity, especially in natural language processing (NLP) tasks.

Why the other options are incorrect:

A: Word clouds visualize word frequency, not similarity.

C: String indexing is a method for referencing string positions, not comparison.

D: k-NN is a classification algorithm, not a string similarity measure.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 6.3:“Edit distance is a key similarity metric in text comparison tasks, particularly in cleaning or matching string records.”

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→ A Type II error occurs when the model fails to identify a positive instance — in this case, a cat. That is, it incorrectly classifies a cat (positive class) as a dog (negative class). This is also referred to as a false negative.

Why the other options are incorrect:

A: “Error due to reality” is not a recognized statistical concept.

B: A false positive would mean misclassifying a dog as a cat (opposite error).

C: Sampling error refers to discrepancies between the sample and population, not a misclassification.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 1.5:“Type II errors occur when a model incorrectly identifies a true positive as a negative — also known as a false negative.”

Pattern Recognition and Machine Learning, Chapter 9:“In binary classification, a Type II error means failing to detect a positive class instance, leading to a false negative result.”

→ 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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Part 1

Linear regression.

Of the four models, linear regression has the highest R² (0.8), indicating it explains the greatest proportion of variance in sales.

Part 2

Var 4 – Net operations cost.

Net operations cost has a p-value of essentially 0 (far below 0.05), indicating it is the only additional predictor statistically significant in explaining sales. Neither inventory cost (p≈0.90) nor initial investment (p≈0.23) reach significance.

Scheduling problems typically involve the assignment of limited resources (e.g., time, personnel, machines) over time to tasks, often under constraints. These problems are inherently mathematical and are typically solved using:

→ Constrained Optimization – which is a mathematical technique for optimizing an objective function subject to one or more constraints. This tool is widely used for operations research problems such as scheduling, resource allocation, logistics, and supply chain optimization.

Why the other options are incorrect:

A. One-armed bandit: Refers to a class of algorithms used for balancing exploration and exploitation, not scheduling.

C. Decision tree: Used for classification and regression, not for constraint-based scheduling.

D. Gradient descent: An optimization method for training models (typically ML), but not specifically suitable for complex constraint-based scheduling.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 3.4 (Modeling Tools):“Scheduling and allocation problems are best addressed using constrained optimization techniques which allow incorporation of resource limits and goal functions.”

Data Science and Operations Research Foundations, Chapter 7:“Constraint-based optimization is the primary mathematical strategy used in scheduling problems to meet deadlines, minimize cost, or maximize throughput.”

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→ A CROSS JOIN returns the Cartesian product of the two tables — meaning every row from the first table is paired with every row from the second table. If Table A has m rows and Table B has n rows, a CROSS JOIN will return m × n rows, making it the largest possible result set of all JOIN types.

Why the other options are incorrect:

A & B: RIGHT JOIN and LEFT JOIN return matched records plus unmatched rows from one side — but not all possible combinations.

D: INNER JOIN returns only matched rows between tables, typically producing fewer records than a CROSS JOIN.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.2:“CROSS JOINs generate the Cartesian product of two datasets and should be used carefully due to the exponential growth in the number of records.”

SQL for Data Scientists, Chapter 3:“CROSS JOINs can produce very large datasets, often unintentionally, due to their non-restrictive matching logic.”

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→ Naive Bayes is a probabilistic classification algorithm based on Bayes’ theorem. It is lightweight, fast, and effective for text-based classification problems like spam detection. It also performs well with small or simple training sets.

Why the other options are incorrect:

A: Logistic regression is also probabilistic but requires more feature preprocessing.

B: Random forest is accurate but computationally heavier.

D: Linear regression is for continuous targets — not suitable for classification.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.1:“Naive Bayes classifiers are ideal for spam detection and similar applications due to their efficiency and probabilistic nature.”

Text Classification Techniques, Chapter 4:“Naive Bayes requires minimal training and works well with high-dimensional, sparse data such as email content.”