DY0-001シュミレーション問題集 & DY0-001ファンデーション一日も早くCompTIAのDY0-001試験に合格したい？ JPTestKingが提供した問題と解答はIT領域のエリートたちが研究して、実践して開発されたものです。それは十年過ぎのIT認証経験を持っています。JPTestKingは全面的な認証基準のトレーニング方法を追求している。JPTestKingのCompTIAのDY0-001を利用した大勢の人々によると、CompTIAのDY0-001試験の合格率は１００パーセントに達したのです。もし君が試験に関する問題があれば、私たちは最も早い時間で、解答します。 CompTIA DataX Certification Exam 認定 DY0-001 試験問題 (Q72-Q77):質問 # 72
Given a logistics problem with multiple constraints (fuel, capacity, speed), which of the following is the most likely optimization technique a data scientist would apply?
A. Non-iterative
B. Constrained
C. Iterative
D. Unconstrained
正解：B
解説：
# This is a classic constrained optimization problem: the boats have fuel, volume, and speed constraints. The goal is to maximize box transport within the fixed limits (e.g., fuel). Constrained optimization methods are explicitly designed to handle such problems.
Why other options are incorrect:
* B: Unconstrained methods do not account for fuel or capacity limits - inappropriate.
* C: Most real-world constrained problems require iterative approaches for convergence.
* D: Iterative may be part of solving, but it's not a type of optimization - constrained is the category.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.4:"Constrained optimization is used when variables must meet certain limitations or bounds."
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質問 # 73
Which of the following best describes the minimization of the residual term in a LASSO linear regression?
A. e
B. e²
C. |e|
D. 0
正解：B
解説：
# LASSO (Least Absolute Shrinkage and Selection Operator) regression minimizes the squared residuals (e²), just like OLS, but adds an L1 penalty to encourage sparsity in the coefficients. Thus, the residual component minimized is still the sum of squared errors.
Why the other options are incorrect:
* A: |e| is absolute error, not used in standard LASSO objective.
* B: e is the error term, but minimization applies to its squared version.
* C: Minimizing to exactly 0 is idealistic but not realistic.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.3:"LASSO minimizes squared errors with an additional L1 regularization term."
* Elements of Statistical Learning, Chapter 6:"LASSO regression uses the same residual sum of squares (e²) as OLS for error measurement, with an added constraint."
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質問 # 74
Which of the following JOINS would generate the largest amount of data?
A. INNER JOIN
B. LEFT JOIN
C. RIGHT JOIN
D. CROSS JOIN
正解：D
解説：
# 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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質問 # 75
A data scientist is building a forecasting model for the price of copper. The only input in this model is the daily price of copper for the last ten years. Which of the following forecasting techniques is the most appropriate for the data scientist to use?
A. Autoregressive
B. Dynamic time warping
C. Moving average
D. Relative strength
正解：A
解説：
# An Autoregressive (AR) model is ideal when past values of a time series are used to predict future values.
Since the only input is historical price data of copper, AR is the most appropriate technique.
Why the other options are incorrect:
* B: Moving average smooths noise but doesn't model the dependencies for prediction.
* C: Dynamic time warping is used for measuring similarity between time series, not forecasting.
* D: Relative strength is a financial metric used for comparing asset performance - not a forecasting technique.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.5:"Autoregressive models are used when the goal is to predict future values based solely on past values in a univariate time series."
* Time Series Analysis and Forecasting, Chapter 5:"AR models capture the temporal dependencies in time series data and are foundational in time-based prediction."
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質問 # 76
Which of the following measures would a data scientist most likely use to calculate the similarity of two text strings?
A. String indexing
B. Edit distance
C. Word cloud
D. k-nearest neighbors
正解：B
解説：
# 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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