CompTIA Data+ DY0-001 Updated Exam

→ Optical Character Recognition (OCR) is the process of converting scanned images or hard copy text into machine-encoded text. It is the standard technique for digitizing printed or handwritten content.

Why the other options are incorrect:

A: Word2vec is for generating word embeddings from digital text.

C: Latent Semantic Analysis analyzes semantic structure of existing digital documents.

D: Semantic segmentation is used in image processing for pixel-wise classification — not text extraction.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 6.3:“OCR converts scanned physical documents into text files that can be searched, analyzed, or stored digitally.”

Practical NLP Applications, Chapter 2:“OCR is a prerequisite for turning printed or written material into structured data suitable for text analytics.”

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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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→ The provided equation represents an autoregressive model of order 1 (AR(1)). It describes Xt as a function of its immediately prior value (Xt−1) plus white noise.

Key identifiers:

No differencing (so not ARIMA).

No moving average term (so not ARMA).

No seasonal component (so not SARIMA).

Why the other options are incorrect:

A: ARIMA(1,1,1) includes integration and MA terms, which are absent here.

B: ARMA(1,1) includes both AR and MA terms, but only AR is present.

C: SARIMA involves seasonal and differencing components — not applicable here.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.5:“AR(p) models describe a variable as dependent on its previous values with no differencing or moving average.”

Time Series Analysis Textbook, Chapter 4:“Xt = ϕXt-1 + εt describes an AR(1) process when εt is white noise.”

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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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