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あなたはどのように毎日を過ごしますか。もちろん、人によって違う方式で毎日過ごします。DY0-001試験の為に、毎日長い時間がかからなければなりません。しかし、DY0-001問題集を利用すれば、たくさんの時間を節約できます。そして、DY0-001問題集は有効的で、大勢のこの問題集を利用したお客様はDY0-001試験に合格しました。信頼に値する資料です。
質問 # 36
A team is building a spam detection system. The team wants a probability-based identification method without complex, in-depth training from the historical data set. Which of the following methods would best serve this purpose?
正解：B
解説：
Naive Bayes directly computes class probabilities using simple frequency counts under the independence assumption, requiring minimal training complexity and no iterative optimization-ideal for fast, probability‐based spam detection.
質問 # 37
A data scientist trained a model for departments to share. The departments must access the model using HTTP requests. Which of the following approaches is appropriate?
正解：A
解説：
Exposing the model behind an HTTP endpoint (for example, a REST API) allows other departments to send requests and receive predictions directly over HTTP. The other options don't inherently provide a request-response interface for sharing a model.
質問 # 38
Which of the following types of machine learning is a GPU most commonly used for?
正解：B
解説：
# GPUs (Graphics Processing Units) are optimized for parallel computations, which are essential for training deep neural networks. These models involve massive matrix operations across multiple layers, making GPUs significantly faster than CPUs in deep learning tasks.
Why the other options are incorrect:
* B: Clustering (e.g., k-means) can benefit from acceleration but doesn't usually require GPU-level computation.
* C: NLP tasks may use GPUs if they involve deep learning (e.g., transformers), but the correct choice is the model type.
* D: Tree-based models (e.g., decision trees, random forests) typically run efficiently on CPUs.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 4.3:"Deep learning models, such as neural networks, are computationally intensive and commonly require GPUs for efficient training."
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質問 # 39
A statistician notices gaps in data associated with age-related illnesses and wants to further aggregate these observations. Which of the following is the best technique to achieve this goal?
正解：C
解説：
# Binning (also known as discretization) involves grouping continuous variables into categories or bins. This technique is useful for aggregation, especially when analyzing trends across ranges (e.g., age groups: 0-18,
19-35, etc.).
In this case, aggregating observations by age ranges would help analyze age-related illnesses more clearly.
Why the other options are incorrect:
* A: Label encoding is used to convert categorical values into numeric codes.
* B: Linearization generally refers to transforming non-linear relationships into linear ones - not relevant here.
* D: Imputing fills missing values, not aggregates or groups them.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.3:"Binning is used to group continuous data for summarization or pattern discovery. Often used in demographic analysis such as age ranges."
* Data Science for Business - Chapter 5:"Discretization simplifies complex continuous variables into interpretable categories, enhancing visualization and trend detection."
質問 # 40
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?
正解：D
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 # 41
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