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| Section | Weight | Objectives |
|---|---|---|
| Modeling, Analysis, and Outcomes | 24% | - Data preparation and exploratory data analysis - Predictive and prescriptive analytics - Model selection and evaluation metrics - Feature engineering and selection - Result interpretation and business communication |
| Mathematics and Statistics | 17% | - Probability theory and distributions - Statistical inference and hypothesis testing - Bayesian reasoning and modeling - Linear algebra fundamentals - Calculus and optimization concepts |
| Operations and Processes | 22% | - Version control and reproducibility - Security and compliance in data operations - Model deployment and monitoring - Data governance and quality management - Data pipeline design and maintenance |
| Machine Learning | 24% | - Deep learning fundamentals - Hyperparameter tuning and optimization - Supervised, unsupervised, and reinforcement learning - Algorithm selection and implementation - Ethics and bias in machine learning |
| Specialized Applications of Data Science | 13% | - Time-series analysis - Natural Language Processing (NLP) - Industry-specific analytics use cases - Computer Vision - Anomaly detection and signal processing |
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NEW QUESTION # 72
Given matrix
Which of the following is AT?
Answer: B
Explanation:
Transposing swaps rows and columns, so the (i, j) entry becomes the (j, i) entry.
NEW QUESTION # 73
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?
Answer: A
Explanation:
# 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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NEW QUESTION # 74
The most likely concern with a one-feature, machine-learning model is high error due to:
Answer: D
Explanation:
A model with only one feature is unlikely to capture the true complexity of the data's underlying relationships, leading to systematic underfitting - i.e., high bias.
NEW QUESTION # 75
A data analyst wants to use compression on an analyzed data set and send it to a new destination for further processing. Which of the following issues will most likely occur?
Answer: B
Explanation:
Compression and decompression are CPU‐intensive operations; on large data sets, the extra processing load can significantly spike CPU utilization. Memory, OS support, or library dependencies are far less likely to be the primary bottleneck in a standard compression workflow.
NEW QUESTION # 76
Which of the following layer sets includes the minimum three layers required to constitute an artificial neural network?
Answer: C
Explanation:
By definition, an artificial neural network requires at least these three fundamental layers: the input layer to receive data, one or more hidden layers to perform transformations, and the output layer to produce predictions. Pooling, convolutional, and dropout layers are useful in specialized architectures (e.g., CNNs) but aren't part of the minimal ANN structure.
NEW QUESTION # 77
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